Sensitivity of phytoplankton to climatic factors in a large shallow lake revealed by column-integrated algal biomass from long-term satellite observations

Temporal shifts in the phytoplankton network in a large eutrophic shallow freshwater lake subjected to major environmental changes due to human interventions

Phytoplankton communities are crucial components of aquatic ecosystems, and since they are highly interactive, they always form complex networks. Yet, our understanding of how interactive phytoplankton networks vary through time under changing environmental conditions is limited. Using a 29-year (339 months) long-term dataset on Lake Taihu, China, we constructed a temporal network comprising monthly sub-networks using "extended Local Similarity Analysis" and assessed how eutrophication, climate change, and restoration efforts influenced the temporal dynamics of network complexity and stability. The network architecture of phytoplankton showed strong dynamic changes with varying environments. Our results revealed cascading effects of eutrophication and climate change on phytoplankton network stability via changes in network complexity. The network stability of phytoplankton increased with average degree, modularity, and nestedness and decreased with connectance. Eutrophication (increasing nitrogen) stabilized the phytoplankton network, mainly by increasing its average degree, while climate change, i.e., warming and decreasing wind speed enhanced its stability by increasing the cohesion of phytoplankton communities directly and by decreasing the connectance of network indirectly. A remarkable shift and a major decrease in the temporal dynamics of phytoplankton network complexity (average degree, nestedness) and stability (robustness, persistence) were detected after 2007 when numerous eutrophication mitigation efforts (not all successful) were implemented, leading to simplified phytoplankton networks and reduced stability. Our findings provide new insights into the organization of phytoplankton networks under eutrophication (or re-oligotrophication) and climate change in subtropical shallow lakes.

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.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R² = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R² = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R² = 0.85, MAPE = 22.68 %; XGBoost: R² = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R² = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R² = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Nutrient reduction mitigated the expansion of cyanobacterial blooms caused by climate change in Lake Taihu according to Bayesian network models

Although nutrient reduction has been used for lake eutrophication mitigation worldwide, the use of this practice alone has been shown to be less effective in combatting cyanobacterial blooms, primarily because of climate change. In addition, quantifying the climate change contribution to cyanobacterial blooms is difficult, further complicating efforts to set nutrient reduction goals for mitigating blooms in freshwater lakes. This study employed a continuous variable Bayesian modeling framework to develop a model to predict spring cyanobacterial bloom areas and frequencies (the responses) using nutrient levels and climatic factors as predictors. Our results suggested that both spring climatic factors (e.g., increasing temperature and decreasing wind speed) and nutrients (e.g., total phosphorus) played vital roles in spring blooms in Lake Taihu, with climatic factors being the primary drivers for both bloom areas and frequencies. Climate change in spring had a 90% probability of increasing the bloom area from 35 km² to 180 km² during our study period, while nutrient reduction limited the bloom area to 170 km², which helped mitigate expansion of cyanobacterial blooms. For lake management, to ensure a 90% probability of the mean spring bloom areas remaining under 154 km² (the 75th percentile of the bloom areas in spring), the total phosphorus should be maintained below 0.073 mg·L^-1 under current climatic conditions, which is a 46.3% reduction from the current level. Our modeling approach is an effective method for deriving dynamic nutrient thresholds for lake management under different climatic scenarios and management goals.

A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms

The worldwide expansion of phytoplankton blooms has severely threatened water quality, food webs, habitat stability and human health. Due to the rapidity of phytoplankton migration and reproduction, high-frequency information on phytoplankton bloom dynamics is crucial for their forecasting, treatment, and management. While several approaches involving satellites, in situ observations and automated underwater monitoring stations have been widely used in the past several decades, they cannot fully provide high-frequency and continuous observations of phytoplankton blooms at low cost and with high accuracy. Thus, we propose a novel ground-based remote sensing system (GRSS) that can monitor real-time chlorophyll a concentrations (Chla) in inland waters with a high frequency. The GRSS mainly consists of three platforms: the spectral measurement platform, the data-processing platform, and the remote access control, display and storage platform. The GRSS is capable of obtaining a remote sensing irradiance ratio (R(λ)) of 400-1000 nm at a high frequency of 20 s. Eight different Chla retrieval algorithms were calibrated and validated using a dataset of 481 pairs of GRSS R(λ) and in situ Chla measurements collected from four inland waters. The results showed that random forest regression achieved the best performance in deriving Chla (R² = 0.95, root mean square error = 13.40 μg/L, and mean relative error = 25.7%). The GRSS successfully captured two typical phytoplankton bloom events in August 2021 with rapid changes in Chla from 20 μg/L to 325 μg/L at the minute level, highlighting the critical role that this GRSS can play in the high-frequency monitoring of phytoplankton blooms. Although the algorithm embedded into the GRSS may be limited by the size of the training dataset, the high-frequency, continuous and real-time data acquisition capabilities of the GRSS can effectively compensate for the limitations of traditional observations. The initial application demonstrated that the GRSS can capture rapid changes of phytoplankton blooms in a short time and thus will play a critical role in phytoplankton bloom management. From a broader perspective, this approach can be extended to other carriers, such as aircraft, ships and unmanned aerial vehicles, to achieve the networked monitoring of phytoplankton blooms.

Spatiotemporal Distribution Pattern of Phytoplankton Community and Its Main Driving Factors in Dongting Lake, China—A Seasonal Study from 2017 to 2019

As it is the second-largest freshwater lake downstream of the Three Gorges Dam and an important international wetland for migratory birds, there have been concerns about the ecological water health of Dongting Lake for a long time. In the present study, we studied the evolutionary characteristics of water quality in Dongting Lake in three recent years. Moreover, the evolution rules and dominant groups of the phytoplankton community were explored, and the major influencing factors of phytoplankton and their distribution were assessed based on the field survey and detection data from 2017 to 2019. The results indicated that the water quality of Dongting Lake improved in recent years. The concentration of dissolved oxygen (DO) increased by 6.91%, whereas the concentrations of the five-day biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), ammonia nitrogen (NH4+–N), total phosphorus (TP), and total nitrogen (TN) decreased by 17.5%, 13.0%, 33.8%, 7.6%, and 13.3%, respectively. The mean phytoplankton density reached 4.15 × 105 cells·L−1 in September 2017, whereas it was only 1.62 × 105 cells·L−1 in December 2018. There were 15 dominant species belonging to Cyanobacteria, Chlorophyta, Bacillariophyta, Cryptophyta, and Miozoa. Moreover, Fragilaria radians (Kützing) D.M.Williams & Round and Aulacoseiragranulata (Ehrenberg) Simonsen were the dominant populations in all seasons. The Pearson and linear regression analysis also indicated that the composition and distribution of phytoplankton in Dongting Lake were mainly affected by electrical conductivity (Cond), BOD5, potassium permanganate (CODMn), and CODCr, especially in Eastern Dongting Lake. Of course, NH4+–N, TN, and TP were also the main factors affecting the density and species of the phytoplankton community, especially in Western Dongting Lake. Finally, we suggested that local government could take “The relationship between Yangtze River and Dongting Lake”, “The relationship between the seven fed rivers and Dongting Lake”, and “The relationship between human activities and Dongting Lake” as the breakthrough points to guarantee the ecological flow, water environment, and ecological quality of Dongting Lake.

Estimation of the lake trophic state index (TSI) using hyperspectral remote sensing in Northeast China

The Trophic state index (TSI) is a vital parameter for aquatic ecosystem assessment. Estimating TSI by remote sensing is still a challenge due to the multivariate complexity of the eutrophication process. A comprehensive in situ spectral-biogeochemical dataset for 7 lakes in Northeast China was collected in October 2020. The dataset covers trophic states from oligotrophic to eutrophic, with a wide range of total phosphorus (TP, 0.07-0.2 mg L^-1), Secchi disk depth (SDD, 0.1-0.78 m), and chlorophyll a (Chla, 0.11-20.41 μg L^-1). Here, we propose an empirical method to estimate TSI from remote sensing data. First, TP, SDD, and Chla were estimated by band ratio/band combination models. Then TSI was estimated using the Carlson model with a high R² (0.88), a low RMSE (3.87), and a low MRE (6.83%). Synergistic effects between TP, SDD, and Chla dominated the trophic state, changed the distribution of light in the water column, affected the spectral characteristics. Furthermore, the contribution of each parameter for eutrophication were different among the studied lakes from ternary plot. High Chla concentration was the main reason for eutrophication in HMT Lake with 45.4% of contribution more than the other two parameters, However, in XXK Lake, high TP concentrations were the main reason for eutrophication with 66.8% of contribution rather than Chla and SDD. Overall, the trophic state was dominated by TP, and SDD accounted for 85.6% of contribution in all sampled lakes. Additionally, we found using one-parameter index to evaluate the lake trophic state will lead to a great deviation, even with two levels of difference. Therefore, multi-parameter TSI is strongly recommended for the lake trophic state assessment. Summarily, our findings provide a theoretical and methodological basis for future large-scale estimations of lake TSI using satellite image data, help with water quality monitoring and management.