Long-term and inter-monthly dynamics of aquatic vegetation and its relation with environmental factors in Taihu Lake, China

Effects of modified sediments on the growth of submerged macrophyte Vallisneria natans under low light conditions

Submerged plants are an important part of aquatic ecosystems, and the restoration of submerged plants is a key step in the reconstruction of aquatic ecosystems. However, little is known about the role of modified sediments in helping submerged plants recover under low light. In this study, we set up four sediment types and two light intensities to explore the effects of modified sediments on the growth of Vallisneria natans under two low light conditions. The results showed that the independent and interactive effects of light intensity and sediment type significantly affected the biomass, morphology, photosynthetic pigment content and antioxidant enzyme activity of V. natans. At 5% and 20% natural light intensity, the sediment modified with 40% peat soil had a larger root biomass and the highest leaf and root C/N ratio, the sediment modified with 40% vermiculite had a longer root length and more ramets. At 5% natural light intensity, the sediments modified with fly ash had shorter root length and smaller leaf biomass. The sediments modified with fly ash had the greatest chlorophyll content at 20% natural light intensity. It can be concluded that the addition of 40% peat soil or 40% vermiculite in sediment is conducive to the growth of V. natans under low light conditions. Our study indicates the positive effects of the modified sediment on the growth of V. natans under low light conditions, and our study will provide a reference for the restoration of submerged plants in aquatic ecosystems.

Hydrometeorological conditions drive long-term changes in the spatial distribution of Potamogeton crispus in a subtropical lake

Globally, anthropogenic disturbance and climate change caused a rapid decline of submerged macrophytes in lake ecosystems. Potamogeton crispus (P. crispus), a species that germinates in winter, explosively expanded throughout many Chinese lakes, yet the underlying mechanism remained unclear. Here, this study examined the long-term changes in the distribution patterns of P. crispus in Lake Gaoyou by combining remote sensing images and hydrometeorological data from 1984 to 2022 and water quality data from 2009 to 2022. It aims to unravel the relationships between the distribution patterns of P. crispus and hydrometeorological and water quality factors. The results showed that the area of P. crispus in Lake Gaoyou showed a slight increase from 1984 to 2009, a marked increase from 2010 to 2019, followed by a decline after 2020. Spatially, P. crispus was primarily distributed in the western and northern parts of Lake Gaoyou, with less distribution in the central and southeastern parts of the lake. Wind speed (WS), temperature (Temp), water level (WL), ammonia nitrogen (NH3-N), and Secchi depth (SD) were identified as the key factors regulating the variation in the P. crispus area in Lake Gaoyou. We found that the P. crispus area showed an increasing trend with increasing Temp, WL, and SD and decreasing WS and NH3-N. The influence of environmental factors on the area of P. crispus in Lake Gaoyou varied among seasons. The results indicated that hydrometeorology (WS, Temp, and WL) may override water quality (NH3-N and SD) in driving the succession of P. crispus distribution. The findings of this study offer valuable insights into the recent widespread expansion of P. crispus in shallow lakes across Eastern China.

Decomposition of exotic versus native aquatic plant litter in a lake littoral zone: Stoichiometry and life form analyses

The decomposition rates and stoichiometric characteristics of many aquatic plants remain unclear, and our understanding of material flow and nutrient cycles within freshwater ecosystems is limited. In this study, an in-situ experiment involving 23 aquatic plants (16 native and 7 exotic species) was carried out via the litter bag method for 63 days, during which time the mass loss and nutrient content (carbon (C), nitrogen (N), and phosphorus (P)) of plants were measured. Floating-leaved plants exhibited the highest decomposition rate (0.038 ± 0.002 day-1), followed by submerged plants and free-floating plants (0.029 ± 0.002 day-1), and emergent plants had the lowest decomposition rate (0.019 ± 0.001 day-1). Mass loss by aquatic plants correlated with stoichiometric characteristics; the decomposition rate increased with an increasing P content and with a decreasing C content, C:N ratio, and C:P ratio. Notably, the decomposition rate of submerged exotic plants (0.044 ± 0.002 day-1) significantly exceeded that of native plants (0.026 ± 0.004 day-1), while the decomposition rate of emergent exotic plants was 55 ± 4 % higher than that of native plants. The decomposition rates of floating-leaved and free-floating plants did not significantly differ between the native and exotic species. During decomposition, emergent plants displayed an increase in C content and a decrease in N content, contrary to patterns observed in other life forms. The P content decreased for submerged (128 ± 7 %), emergent (90 ± 5 %), floating-leaved (104 ± 6 %), and free-floating plants (32 ± 6 %). Exotic plants released more C and P but accumulated more N than did native plants. In conclusion, the decomposition of aquatic plants is closely linked to litter quality and influences nutrient cycling in freshwater ecosystems. Given these findings, the invasion of the littoral zone by submerged and emergent exotic plants deserves further attention.

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Spatial and temporal patterns of phytoplankton community succession and characteristics of realized niches in Lake Taihu, China

Understanding the dynamics and succession of phytoplankton in large lakes can help inform future lake management. The study analyzed phytoplankton community variations in Lake Taihu over a 21-year period, focusing on realized niches and their impact on succession. The study developed a niche periodic table with 32 niches, revealing responses to environmental factors and the optimal number of niches. Results showed that the phytoplankton in Lake Taihu showed significant spatial and temporal heterogeneity, with biomass decreasing as one moved from the northwest to the southeast and expanding towards central lake area, and towards autumn and winter. Different phytoplankton groups in Lake Taihu occupied realized niches shaped by temperature, nitrate, and phosphate. To predict the response of eutrophic freshwater lake ecosystems to human activities and climate change, it is critical to interpret the law of phytoplankton bloom and niche succession.

Exploring invertebrate indicators of ecosystem health by focusing on the flow transitional zones in a large, shallow eutrophic lake

The river-lake transitional zone provides a unique environment for the biological community and can reduce pollution inputs in lake ecosystems from their catchments. To explore environmental conditions with high purification potential in Lake Taihu and indicator species, we examined the river-to-lake changes in water and sediment quality and benthic invertebrate communities in the transitional zone of four regions. The spatial variations in the environment and invertebrate community observed in this study followed the previously reported patterns in Taihu; the northern and western regions were characterized by higher nutrient concentrations in water, higher heavy metal concentrations in sediment, and higher total invertebrate density and biomass dominated by pollution-tolerant oligochaetes and chironomids. Although nutrient concentrations were low and transparency was high in the eastern region, the taxon richness was the lowest there, which disagreed with the previous findings and might be due to a poor cover of macrophytes in this study. The river-to-lake change was large in the southern region for water quality and the invertebrate community. Water circulation induced by strong wind-wave actions in the lake sites of the southern region is assumed to have promoted photosynthetic and nutrient uptake activities and favored invertebrates that require well-aerated conditions such as polychaetes and burrowing crustaceans. Invertebrates usually adapted to brackish and saline environments are suggested to be indicators of a well-circulated environment with active biogeochemical processes and a less eutrophic state in Taihu, and wind-wave actions are key to maintaining such a community and natural purifying processes.

Driving forces and recovery potential of the macrophyte decline in East Taihu Lake

Macrophytes are of key importance to the structure and ecological services of shallow lakes and are sensitive to anthropogenic and natural perturbations. Ongoing eutrophication and hydrological regime change affect macrophytes through changes in water transparency and water level, which lead to a dramatic decrease in bottom light availability. Here an integrated dataset (2005-2021) of multiple environmental factors is used to demonstrate the driving forces and recovery potential of the macrophyte decline in East Taihu Lake by using a critical indicator, which is the ratio of the Secchi disk depth to the water depth (SD/WD). The macrophyte distribution area showed a remarkable decrease from 136.1 ± 9.7 km² (2005-2014) to 66.1 ± 6.5 km² (2015-2021). The macrophyte coverage in the lake and in the buffer zone decreased by 51.4% and 82.8%, respectively. The structural equation model and correlation analysis showed that the distribution and coverage of macrophytes decreased with the decrease in the SD/WD over time. Moreover, an extensive hydrological regime change, which caused a sharp decrease in SD and an increase in the water level, is likely to be the driving force that brought about the decline of macrophytes in this lake. The proposed recovery potential model shows that the SD/WD has been low in recent years (2015-2021), and that this SD/WD cannot ensure the growth of submerged macrophytes and is unlikely to ensure the growth of floating-leaved macrophytes, especially in the buffer zone. The approach developed in the present study provides a basis for the assessment of macrophyte recovery potential and the management of ecosystems in shallow lakes that suffer from macrophyte loss.

Global historical trends and drivers of submerged aquatic vegetation quantities in lakes

Submerged aquatic vegetation (SAV) in lake littoral zones is an inland water wetland type that provides numerous essential ecosystem services, such as supplying food and habitat for fauna, regulating nutrient fluxes, stabilizing sediments, and maintaining a clear water state. However, little is known on how inland SAV quantities are changing globally in response to human activities, where loss threatens the provisioning of these ecosystem services. In this study, we generate a comprehensive global synthesis of trends in SAV quantities using time series (>10 years) in lakes and identify their main drivers. We compiled trends across methods and metrics, integrating both observational and paleolimnological approaches as well as diverse measures of SAV quantities, including areal extent, density, or abundance classes. The compilation revealed that knowledge on SAV is mostly derived from temperate regions, with major gaps in tropical, boreal, and mountainous lake-rich regions. Similar to other wetland types, we found that 41% of SAV times series are largely decreasing mostly due to land use change and resulting eutrophication. SAV is, however, increasing in 28% of cases, primarily since the 1980s. We show that trends and drivers of SAV quantities vary regionally, with increases in Europe explained mainly by management, decreases in Asia due to eutrophication and land use change, and variable trends in North America consistent with invasive species arrival. By providing a quantitative portrait of trends in SAV quantities worldwide, we identify knowledge gaps and future SAV research priorities. By considering the drivers of different trends, we also offer insight to future lake management related to climate, positive restoration actions, and change in community structure on SAV quantities.

Impact of submerged macrophytes on growth and 2-MIB release risk of Pseudanabaena sp.: From field monitoringa to cultural experiments

The off-flavor compound 2-methylisoborneol (2-MIB) is generally associated with the proliferation and metabolism of filamentous cyanobacteria in shallow freshwater ecosystems. Here field monitoring in East Taihu Lake from July to October 2021, along with cultural experiments, was conducted to determine the impact of submerged macrophytes on the growth and 2-MIB production of filamentous cyanobacteria. Pseudanabaena sp. was identified as the 2-MIB producer with the highest detection rate (100%) and correlation coefficient (R=0.68, p < 0.001). The 2-MIB concentration and algal growth in the macrophyte-dominated zones were markedly decreased compared with those in the phytoplankton-dominated zone. Five submerged macrophytes classified into flat-leaf type (Vallisneria natans and Potamogeton crispus) and thin-leaf type (Hydrilla verticillata, Ceratophyllum demersum, and Myriophyllum spicatum) exhibited strong inhibition effects against Pseudanabaena sp.: Overall inhibition efficiencies (IEs) of 92.7% ± 6.8% and 92.7% ± 8.4% for cell growth and 2-MIB production were achieved, respectively. Moreover, the thin-leaf macrophytes exhibited significant higher IEs for cell growth (94.0% vs. 84.7%) and 2-MIB production (99.4% vs. 82.6%) than the flat-leaf macrophytes and can be selected as pioneer species in controlling odor problems. Nutrient uptake, increasing water clarity, shading effects, and allelopathic effects of the submerged macrophytes were found to be the dominant inhibition mechanisms.

A hybrid remote sensing approach for estimating chemical oxygen demand concentration in optically complex waters: A case study in inland lake waters in eastern China

Chemical oxygen demand concentration (C_COD) is widely used to indicate the degree of organic pollution of lakes, reservoirs and rivers. Mastering the spatiotemporal distribution of C_COD is imperative for understanding the variation mechanism and controlling of organic pollution in water. In this study, a hybrid approach suitable for Sentinel 3A/Ocean and Land Colour Instrument (OLCI) data was developed to estimate C_COD in inland optically complex waters embedding the interaction between C_COD and the absorption coefficients of optically active constituents (OACs). Based on in-situ sampling in different waters, the independent validations of the proposed model performed satisfactorily in Lake Taihu (MAPE = 23.52 %, RMSE = 0.95 mg/L, and R² = 0.81), Lake Qiandaohu (MAPE = 21.63 %, RMSE = 0.50 mg/L and R² = 0.69), and Yangtze River (MAPE = 29.34 %, RMSE = 0.83 mg/L, and R² = 0.64). In addition, the approach not only showed significant superiority compared with previous algorithms, but also was suitable for other common satellite sensors equipped same or similar bands. The hybrid approach was applied to OLCI images to retrieve C_COD of Lake Taihu from 2016 to 2020 and reveals substantial interannual and seasonal variations. The above results indicate that the proposed approach is effective and stable for studying spatiotemporal dynamic of C_COD in optically complex waters, and that satellite-derived products can provide reliable information for lake water quality management.

Climate, hydrology, and human disturbance drive long-term (1988-2018) macrophyte patterns in water diversion lakes

Macrophytes are affected by many natural and human stressors globally but their long-term responses to these multiple stressors are not often quantified. We employed remote sensing and statistical tools to analyze datasets from both short-term (2017-2018) field investigations to explore seasonal patterns, and long-term (1988-2018) Landsat remote-sensing images to detect annual patterns of macrophyte distributions and study their responses to changes in climate, hydrology, and anthropogenic activities in a chain of water diversion lakes in eastern China. We found: 1) biomass and species richness of macrophytes peaked in summer with dominant species of submerged macrophytes Ceratophyllum demersum, Potamogeton pectinatus, and Potamogeton maackianus and floating macrophytes Trapa bispinosa, and non-native species Cabomba caroliniana spread in midstream Luoma Lake and Nansi Lake in summer, while Potamogeton crispus was dominant in all the lakes in spring; 2) water physicochemical parameters (chloride and water depth), lake characteristics (area and water storage), climate factors (air temperature and precipitation), and anthropogenic activities (commercial fishery and urban development) were significantly correlated to the seasonal distribution of macrophytes; 3) long-term data showed a significantly negative correlation between coverage of floating macrophytes and precipitation where the wettest year of 2003 had the lowest coverage of floating macrophytes; and 4) climate (air temperature) and hydrology (water level) were positively correlated with total macrophyte coverage, but human disturbance indexed by the gross domestic product was negatively driving long-term coverage of macrophytes. Our study has important implications for understanding the long-term succession of macrophytes under both natural and human stressors, and for future environmental management and ecological restoration of freshwater lakes.

Assessing Climate Influence on Spatiotemporal Dynamics of Macrophytes in Eutrophicated Reservoirs by Remotely Sensed Time Series

The overgrowth of macrophytes is a recurrent problem within reservoirs of urbanized and industrialized areas, a condition triggered by the damming of rivers and other human activities. Although the occurrence of aquatic plants in waterbodies has been widely monitored using remote sensing, the influence of climate variables on macrophyte spatiotemporal dynamics is rarely considered in studies developed for medium scales to long periods of time. We hypothesize that the spatial dispersion of macrophytes has its natural rhythms influenced by climate fluctuations, and, as such, its effects on the heterogeneous spatial distribution of this vegetation should be considered in the monitoring of water bodies. A eutrophic reservoir is selected for study, which uses the Normalized Difference Vegetation Index (NDVI) as a proxy for macrophytes. Landsat’s NDVI long-term time series are constructed and matched with the Climate Variable (CV) from the National Oceanic and Atmospheric Administration (NOAA) to assess the spatiotemporal dynamics of aquatic plants and their associated climate triggers. The NDVI and CV time series and their seasonal and trend components are correlated for the entire reservoir, compartments, and segmented areas of the water body. Granger-causality of these climate variables show that they contribute to describe and predict the spatial dispersion of macrophytes.

Spatiotemporal heterogeneities and driving factors of water quality and trophic state of a typical urban shallow lake (Taihu, China)

Water quality deterioration and eutrophication of urban shallow lakes are global ecological problems with increasing concern and greater environmental efforts. In this study, spatiotemporal changes of water quality and eutrophication were assessed by trophic level index (TLI), cluster analysis, and spatial interpolation methods in Lake Taihu and its sub-lakes from 2015 to 2019. Results showed that the Taihu had poor water quality and maintained a light-eutropher state overall, mainly astricted by the total nitrogen (TN) and the total phosphorus (TP). All nutrient parameters reached relatively higher concentrations in the northwestern and northern areas. Meiliang Bay was the most polluted and nutrient-rich area. In terms of trend, the Mann-Kendall test highlighted that the TP and chlorophyll-a (Chl-a) concentrations increased significantly while the TN and five-day biochemical oxygen demand (BOD5) decreased. The massive nutrient loads caused by human activity from the northwestern Taihu and the geomorphological characteristic of the north closed bays were the main contributors to the spatial heterogeneity in water quality. The main driving force of the alleviative nitrogen pollution was the declining river inflow nitrogen loading, and phosphorus pollution was affected more by accumulated endogenous pollution and decline in aquatic plants area, as well as closely linked with algae biomass. Further water pollution and eutrophication restoration of Taihu should focus on the nutrient reductions and those heavily polluted closed bays.

Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations

Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.

A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China

Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction of the CyanoHABs area in Taihu Lake. Firstly, the time-series data of the CyanoHABs area in Taihu Lake for 20 years were accurately obtained using MODIS images from 2000 to 2019 based on the FAI method. Then, a principal component analysis was performed on the daily meteorological data for the month before the outbreak of CyanoHABs in Taihu Lake from 2000 to 2019 to determine the meteorological factors closely related to the outbreak of CyanoHABs. Finally, the features of CyanoHABs area and meteorological data were extracted by Convolutional Neural Networks (CNN) model and used as the input of Long Short Term Memory Network (LSTM). An integrated CNN-LSTM model approach was constructed for predicting the CyanoHABs area. The results show that high R² (0.91) and low mean relative error (17.42%) verified the validity of the FAI index to extract the CyanoHABs area in Taihu Lake; the meteorological factors closely related to the CyanoHABs outbreak in Taihu Lake are mainly temperature, relative humidity, wind speed, and precipitation; the CNN-LSTM integrated model has better prediction effect for both training and test sets compared with the CNN and LSTM models. This study provides an effective method for predicting temporal changes in the CyanoHABs area and offers new ideas for scientific and effective regulation of inland water safety.

Morphological responses of the submerged macrophyte Vallisneria natans along an underwater light gradient: A mesocosm experiment reveals the importance of the Secchi depth to water depth ratio

Bottom light availability (BLA), represented by the ratio of the Secchi disk depth to water depth (SD/WD), plays a fundamental role in the growth and reproduction of submerged macrophytes. However, studies thus far have mainly explored the interactions between macrophyte responses and BLA through field investigations; this means that knowledge of such responses to various underwater light conditions in mesocosm experiments is rudimentary at best. We hypothesized that the growth and clonal reproduction of submerged macrophytes decrease with decreasing BLA and collapse beyond a critical threshold. Here we performed a 42-day outdoor mesocosm experiment with a species of perennial submerged macrophyte, Vallisneria natans, along a decreasing SD/WD gradient. Over this gradient, the primary morphological traits (plant height, root length, plant biomass), relative growth rate, and shoot increment rate of V. natans exhibited a significant trend of initial increase followed by a decrease. The photoinhibition occurred at high and low-light stress, indicating that an intermediate SD/WD (0.55-0.65) provides optimal growth conditions. The number of ramets, ramet biomass, ramet/total biomass ratio, and root/shoot ratio all decreased with decreasing SD/WD ratio, suggesting that V. natans allocates more resources for clonal reproduction and population stability rather than increased shoot biomass at higher BLA conditions. The results of principal component analysis and threshold detection indicated that the growth traits of V. natans had a higher SD/WD tipping point value (0.55 vs. 0.50) than the reproductive capacity and stability, indicating that only values of SD/WD ≥ 0.55 ensured the growth and the vegetative reproduction of V. natans. Additionally, an inverted U-shaped relationship between growth traits and a linear relationship between reproduction and stability reflect the resource allocation strategies and resilience of V. natans to decreasing underwater light conditions.

Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images

Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology–pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology–pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user’s accuracy and producer’s accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems.

Effect of submerged plant coverage on phytoplankton community dynamics and photosynthetic activity in situ

Restoration of submerged plants in eutrophic lakes can reduce nutrients and phytoplankton biomass in the water body. However, the effect of submerged plants on phytoplankton communities and their photosynthetic activity in situ are still poorly understood. Here, we studied the response of phytoplankton community structure and fluorescence parameters to different submerged plants coverage, the relationship of phytoplankton community and fluorescence parameters with submerged plants coverage and water physicochemical parameters were analysed in sampling area of Hangzhou West Lakes. The results showed that the coverage and biomass of submerged plants were negatively correlated with nitrogen and phosphorus contents in water body but positively correlated with total phenol content. The ratio of nitrogen to phosphorus in the study site changed greatly (32.25-124.54). In spring and summer, Oscillatoria and Leptolyngbya (Cyanophyta) were the dominant species, while in autumn and winter, the dominant species were Cyclotella (Chlorophyta), and Melosira and Cymbella (Bacillariophyta). Compared with Chlorophyta and Bacillariophyta, fluorescence parameters of Cyanophyta were more sensitive to total phosphorus, N:P ratio, total phenols, pH, and electric conductivity. Fluorescence parameters of Chlorophyta and Bacillariophyta were only affected by underwater light. Total phosphorus (TP) and N:P had a negative effect on the maximum photochemical electron yield of Cyanophyta. Furthermore, Cyanophyta was inhibited by total phenols from submerged plants. When phytoplankton were under stress, photochemical electron yield decreased significantly, whereas non-photochemical quenching increased. The structural equation model showed that the coverage of submerged plants might indirectly affect the fluorescence parameters of Cyanophyta by affecting nitrogen, phosphorus, and total phenol contents in the water body. These findings contribute to the understanding of the mechanisms underlying the impact of submerged plant restoration on phytoplankton community dynamics in subtropical eutrophic shallow lakes and provide a theoretical basis for the management of lakes.

The role of mechanical harvesting on the recession of aquatic vegetation under an extreme water level increase in a eutrophic shallow lake

Mechanical harvesting is quick and effective way to remove nuisance macrophytes and improve recreational use and aesthetics in shallow lake. However, applying mechanical harvesting to macrophytes in eutrophic shallow lake with weak resilience and strong perturbation raises concerns of the public and scientific communities. A combination of field investigation and remote sensing was used to determine the potential driving factors of macrophyte degradation in a eutrophic shallow lake from 2014 to 2017, including a comparative analysis of preserved and harvested areas to determine the impacts of mechanical harvesting. Over 95% of macrophytes had disappeared by 2017 in both preserved and harvested areas, with no significant difference in macrophyte distribution area or decline rate between the two by that time. The decline rate in the harvested area (76.7%) was slightly higher than in the preserved area (61.7%) in 2016 after performing the mechanical harvesting in 2015. The results demonstrate that mechanical harvesting is not the definitive driving factor for macrophyte loss, but it could accelerate the decline process. Bottom light availability (Secchi disk depth to water level), which decreased from 0.70 to 0.29 in 2015 and from 0.70 to 0.21 in 2016, is more likely the driving factor, caused by extreme water level increase events in two consecutive years (2015 and 2016) and decreased water clarity. Maintaining water clarity and low water level is crucial for macrophyte restoration.

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

Eutrophication assessment of seasonal urban lakes in China Yangtze River Basin using Landsat 8-derived Forel-Ule index: A six-year (2013-2018) observation

Lakes eutrophication have been a complex and serious problem for China's Yangtze River Basin. A series of algorithms based on different remote sensing dataset have been proposed to simulate the lakes trophic state. However, these algorithms are often targeted at a particular lake and cannot be applied to a watershed management. In this study, a Forel-Ule index (FUI) method based on Landsat 8 OLI image is proposed to simulate trophic state index (TSI) in three typical urban lakes (Dianchi, Donghu, and Chaohu) from 2013 to 2018. The results show that the Landsat 8 derived FUI can well represent the lake TSI with an accuracy of R² = 0.6464 for the in situ experimental TSI dataset (N = 115) and R² = 0.8065 for the lake average TSI dataset (N = 315). In the study period 2013-2018, the order of the simulated TSI is Dianchi > Chaohu > Donghu. Seasonal dynamics show differences where the percentage of eutrophic area in summer is significantly lower than the other seasons for Lake Dianchi and Chaohu. However, the percentage of eutrophic area for Lake Donghu is highest in summer and lowest in winter. To further detect the driving factors of eutrophication in study lakes, the Pearson correlation and multiple linear regression analyses were conducted. The results show that sunshine and temperature are, respectively, the most and the second most significant factors for Lake Dianchi with explanations of 14.8% and 22.0%; temperature and pollution are the main influencing factors for Lake Donghu (39.2% and 10.9% explanation, respectively) and Chaohu (57.2% and 60.7% explanations, respectively). In addition, the wind is another negatively significant factor for Lake Chaohu with an explanation of 31.3%. Our results serve as an example for other lakes in the Yangtze River Basin and support the formulation of effective strategies to reduce seasonal eutrophication.

Transformation of Aquatic Plant Diversity in an Environmentally Sensitive Area, the Lake Taihu Drainage Basin

Located downstream of the Yangtze River Delta, the Lake Taihu drainage basin (LTDB) is one of the most developed areas in China. This area currently faces population and development issues, as well as many environmental problems, such as cultural eutrophication, algal blooms, and loss of native aquatic plants. Changes in aquatic biodiversity have received less attention than have changes in terrestrial habitats because relevant observations are lacking. In this study, information from 2010, 2014, and 2018 concerning the transformation of the aquatic plant biodiversity was obtained. The results showed that the dominant aquatic plants have changed from native plants to invasive plants. Aquatic plant biodiversity showed a decreasing trend, which may reduce the freshwater ecosystem function, and anthropogenic activities accounted for these changes. How to prevent the decline in aquatic plants and control the invasion of introduced aquatic plants should be a priority in the management of aquatic plants in the LTDB.

A First Insight into the Structural and Functional Comparison of Environmental Microbiota in Freshwater Turtle Chinemys reevesii at Different Growth Stages under Pond and Greenhouse Cultivation

The microbial community structure of water is an important indicator for evaluating the water quality of the aquaculture environment. In this study, the investigation and comparison of the bacterial communities of pond cultivation (PC) and greenhouse cultivation (GC) between hatchling, juvenile, and adult growth stages of C. reevesii were performed. In addition, the V4 regions of the 16S rRNA gene were sequenced. The Chao1 richness estimator of the PC group was significantly higher than that of the GC group. The beta diversity showed that the microbiotas of the two groups were isolated from each other. The dominant phyla were Cyanobacteria, Proteobacteria, Actinobacteria, Bacteroidetes, Verrucomicrobia, and Planctomycetes in the PC group and Proteobacteria, Bacteroidetes, Firmicutes, Cyanobacteria, Chloroflexi, and Actinobacteria in the GC group. Both the numbers and the types of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations differed between the PC and GC groups. The prediction of bacterial phenotype implied that the GC environment is more likely to deteriorate, and turtles are more susceptible to pathogens than those of the PC environment. In addition, a total of nine potential pathogenic bacteria were identified and the correlation of environmental factors analyses showed significant differences of bacterial species between the PC and GC groups, while the potential pathogenic bacteria showed significant correlation with the stocking density, temperature, pH, orthophosphate (PO4-P), and dissolved oxygen (DO) in both the PC and GC groups. Noticeably, this is the first report to describe the different microbiota characteristics of the different cultivation environments in the different growth stages of C. reevesii, which will provide valuable data for water quality adjustment, disease prevention, and the healthy breeding of turtles.

Synergetic enhancement toxicity of copper, cadmium and microcystin-LR to the Ceratophyllum demersum L

Heavy metals and microcystins commonly co-exist in water bodies with cyanobacteria, and have been shown to affect aquatic plants. However, their combined effects remain largely unknown. In this study, the toxic effects of copper (Cu) and cadmium (Cd) on Ceratophyllum demersum L. were characterized in the presence of microcystin-LR (MC-LR). The results showed that the bioaccumulation of MC-LR and Cu/Cd in C. demersum was significantly increased by the interaction between MC-LR and Cu/Cd. The combined toxicity assessment results suggested that the toxicities of Cu or Cd to C. demersum would be largely exacerbated by MC-LR, which could be the results of increased bioaccumulation of the pollutants. Cu, Cd and MC-LR, as well as their mixture, significantly decreased plant fresh weight and total chlorophyll content of C. demersum, especially at their high concentrations. The antioxidative system was activated to cope with the adverse effects of oxidative stress. Antioxidant enzyme activities were significantly stimulated by Cu, Cd and MC-LR, as well as their mixture. However, the decreased superoxide dismutase (SOD) and glutathione reductase (GR) activities were observed when exposed to relative high concentrations of Cu or Cd together with MC-LR of 5 μg L^-1. MC-LR brought more stress to the antioxidative system, which is another possible explanation for the synergistic effect. Our findings highlight increased ecological risks of the co-contamination of heavy metals and harmful cyanobacteria.

Automatic Extraction of Open Water Using Imagery of Landsat Series

Open surface freshwater is an important resource for terrestrial ecosystems. However, climate change, seasonal precipitation cycling, and anthropogenic activities add high variability to its availability. Thus, timely and accurate mapping of open surface water is necessary. In this study, a methodology based on the concept of spatial autocorrelation was developed for automatic water extraction from Landsat series images using Taihu Lake in south-eastern China as an example. The results show that this method has great potential to extract continuous open surface water automatically, even when the water surface is covered by floating vegetation or algal blooms. The results also indicate that the second shortwave-infrared band (SWIR2) band performs best for water extraction when water is turbid or covered by surficial vegetation. Near-infrared band (NIR), first shortwave-infrared band (SWIR1), and SWIR2 have consistent extraction success when the water surface is not covered by vegetation. Low filter image processing greatly overestimated extracted water bodies, and cloud and image salt and pepper issues have a large impact on water extraction using the methods developed in this study.

Generalized additive models for biomass simulation of submerged macrophytes in a shallow lake

Submerged macrophytes are widely distributed primary producer that play important roles in maintaining healthy aquatic ecosystems. Generally, the relationships between macrophytes and environmental factors are complicated, so nonlinear nonparametric models with relatively flexible structures are optimal for macrophyte habitat simulation. In this study, generalized additive model (GAM) was used to evaluate the response of the submerged macrophytes biomass to water environmental factors in the Baiyangdian Lake. Forward stepwise method was used to implement model optimization. Likelihood ratio test was used to determine whether adding a variable enhances the model performance. Four individual variables (water depth, transparency, total nitrogen, and total phosphorus) and two interaction terms (water depth × transparency and water depth × total phosphorus) were included in the optimal GAM. The optimal model explained 70.5% of the biomass variation with a relatively low residual deviance value (22.40). There was a significant correlation between the measured and predicted data (R² = 0.716, p = 0.0004). The response lines generated by the model indicated that macrophyte biomass had a positive correlation with transparency but negative correlations with total nitrogen and nitrite nitrogen in water. The response patterns of macrophyte biomass to water depth and total phosphorus were unimodal. The biomass reached the maximum value when the water depth was about 2.1 m and the total phosphorus concentration was 0.07 mg/L. Water depth and transparency, which affect light availability, are critical physical variables affecting the conditions associated with the submerged macrophytes, and excess nitrite and phosphorus limiting macrophyte biomass.

Pen Culture Detection Using Filter Tensor Analysis with Multi-Temporal Landsat Imagery

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases.

Seasonal variation of aquatic macrophytes and its relationship with environmental factors in Baiyangdian Lake, China

The relationships of aquatic macrophytes and 14 environmental factors of the Baiyangdian Lake in spring, summer and autumn were investigated by Pearson correlation analysis and redundancy analysis (RDA) method. The pollutant concentrations in water and sediment were at high level and changed obviously with season. TOC, NO₂-N and NH₄-N exhibited a strong correlation with other environmental factors in water. TC, TOC and TN had a strong correlation with other environmental factor in sediments. The total biomass of aquatic macrophytes reached its maximum value of 2411.6 g/m² in spring and the total species number achieved its maximum in summer. Potamogeton pectinatus was the most dominant species in three seasons. From RDA analysis, the top three water chemical factors that influence aquatic macrophytes were Chla, NO₃-N and TN (contributions of 14.6%, 13.8% and 10.5%, respectively). The top three environmental factors in sediment were TOC, IP and NH₄-N (contributions of 35.6%, 25.7% and 20.7%, respectively). The aquatic macrophytes also had distinct major environmental factors. The diversity indexes of the aquatic macrophytes had less than 0.5 fluctuations. This work might provide more insights into the control and remediation of lake pollution in North China.

Response of Submerged Aquatic Vegetation to Water Depth in a Large Shallow Lake after an Extreme Rainfall Event

Submerged aquatic vegetation (SAV) is an important part of lake ecosystems, and a proper SAV community structure is the key factor in keeping a clear-water state. Although the response of SAV to water depth has been widely studied in different aquatic environments, little is known about the response of the SAV community to changes in water depth of a large lake after an extreme rainfall event. To examine this question, 780 samples were collected from Lake Taihu, China, between 2013 and 2017 to analyze the variations in SAV and water depth. The water level of the lake ranged from 2.75 to 4.87 m, and the water depth at sampling sites ranged from 1.07 to 3.31 m. The SAV biomass at the sampling sites ranged from 0 to 17.61 kg/m2. The influence of water depth on SAV biomass and frequency of occurrence differed by seasons and by species. The adaptation of SAV species to increasing water depth is a key element for community dynamics, which in turn contributes to water level regulation. A new method was proposed to identify the optimal water depth for SAV biomass accumulation based on calculation of the cumulative probability and probability density.

MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi.

Spatiotemporal variation and influencing factors of vegetation cover in the ecologically fragile areas of China from 2000 to 2015: a case study in Shaanxi Province

China's Loess Plateau region has a weak ecological environment, and the government has invested a considerable amount of money to repair the ecological environment. Vegetation plays an important role in the ecological environment. The Sen slope analysis and the Mann-Kendall trend test were used to analyze the trend and significance of vegetation coverage from 2000 to 2015. The vegetation coverage was analyzed to investigate the influence of land use types and conversion. The Pearson Correlation Test and qualitative analysis were utilized at the pixel and regional scales to investigate the influence of meteorological factors and topographical factors. The fluctuation of vegetation in Shaanxi was analyzed from 2000 to 2015. The impact of anthropogenic activities was investigated using residual trend analysis. Hurst exponent and H/S analysis were applied to investigate the potential future vegetation coverage trend. The vegetation coverage in Shaanxi Province improved from 2000 to 2015. In unchanged land use types, all types showed significant improvements expect for other construction land. In changed land use types, most of the land use types converted into urban land showed degradation. All the land use types converted into dry land, forest, and unused land showed improvements. Ecological protection has achieved great results. Precipitation and temperature partly affect vegetation coverage in Shaanxi. Gradients and elevation affected the distribution of vegetation coverage and human activities influenced land use type and the ecological environment. In the future, potential degradation risks still exist in the parts of Shaanxi Province.

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

Is There a Pattern for Occurrence of Macrophytes in Polish Ponds?

Although ponds are common elements in the environment, dependences occurring in these ecosystems have not been fully investigated. Our study focuses on the correlation between environmental factors and changes occurring in ponds—mostly the distribution of macrophytes in space and time. The aim of our analyses was to indicate which physicochemical variables were characteristic of ponds in specific habitats (forest, agriculture field, and village) and whether they could associate the distribution of vegetation in these ponds. Thirteen ponds differing in morphometric parameters and location in landscape (ponds located in agriculture fields, forests, and villages) were analyzed. Our research was based on data covering a period of 10 years (2008–2018). The following parameters were analyzed: the water content of NH4+, NO3−, NO2−, TP, PO43−, Na, K, Mg, Ca, and Fe, pH, temperature and morphometric parameters. Macrophytes were observed during the growing season in July 2008, 2010, 2012, 2014, 2016 and 2018. Three homogeneous species groups were distinguished in statistical analyses and the dominance of selective environmental factors was assigned to them. The first group consisted of indicator species for forest ponds. Their waters were characterized by a low content of Ca, Mg, and Na as well as an increased content of NH4+ and Fe. The second group was composed of indicator species for field ponds, where the highest NO3− concentrations were found. The third group was formed by indicator species for village ponds. Water of these ponds was characterized by higher concentration levels of K, Na, and total P.

Application of a Hybrid Optimized BP Network Model to Estimate Water Quality Parameters of Beihai Lake in Beijing

Nowadays, freshwater resources are facing numerous crises and pressures, resulting from both artificial and natural process, so it is crucial to predict the water quality for the department of water environment protection. This paper proposes a hybrid optimized algorithm involving a particle swarm optimization (PSO) and genetic algorithm (GA) combined BP neural network that can predict the water quality in time series and has good performance in Beihai Lake in Beijing. The data sets consist of six water quality parameters which include Hydrogen Ion Concentration (pH), Chlorophyll-a (CHLA), Hydrogenated Amine (NH4H), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), and electrical conductivity (EC). The performance of the model was assessed through the absolute percentage error ( A P E m a x ), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Study results show that the model based on PSO and GA to optimize the BP neural network is able to predict the water quality parameters with reasonable accuracy, suggesting that the model is a valuable tool for lake water quality estimation. The results show that the hybrid optimized BP model has a higher prediction capacity and better robustness of water quality parameters compared with the traditional BP neural network, the PSO-optimized BP neural network, and the GA-optimized BP neural network.

Conversion of Secondary Forests into Chestnut Forests Affects Soil Nutrients in Anji County, China

The maintenance of drinking water safety is a major environmental issue. It is necessary to strengthen environmental protection in water source areas and establish good vegetation coverage. This study examined the effects of secondary forests transformation on chestnut forests on soil nutrient changes in the Fuji Reservoir, Anji County, Zhejiang province, China. Plots were set up in a chestnut plantation and a nearby secondary forest to measure the nutrient contents of soil samples that were collected from different soil depths. Differences of soil nutrient content from the two stands were significant at 0–20 cm soil depth. There were no significant differences in the contents of total phosphorus and total potassium between the two forests; however, the available phosphorus content in chestnut stands was 2.73 mg/kg higher than in secondary forests. Overall, the soil nutrient contents under chestnut stands were lower than those under secondary forests. Some of the soil surface is exposed due to the low diversity of the chestnut forest. The soil nutrients in the chestnut forest are usually carried and transferred in soil particle form and they become dissolved in the runoff during rainfall and lost, which explains the lower soil nutrient contents in the chestnut forest than the secondary forest. Therefore, for economic forests, such as chestnut forests, measures should be taken to protect understory vegetation and enhance soil and water conservation capacity, which is conducive to retaining soil nutrients.