Secchi Depth Research Articles

Breeding waterbird species as ecological indicators of shifts from turbid to clear water conditions in northwest European shallow eutrophic lakes

AbstractWe used biological and physical responses at 71 shallow waterbodies with contrasting nutrient levels undergoing recovery from eutrophication to predict potential changes in waterbird species abundance, an important component of lake ecosystems. These general predictions were tested using 28 years of breeding waterbird data from three Danish shallow eutrophic lakes, comparing species-specific responses to improved nutrient and water transparency in two lakes with a third where conditions remained constantly suitable for breeding waterbirds. We predicted positive responses to improved water quality from pursuit diving predators (three grebe species), a specialist zooplankton feeder (northern shoveler Anas clypeata) and waterbirds feeding on (common pochard Aythya ferina) or within (tufted duck A. fuligula) submerged macrophyte underwater canopies. These species were characterised by positive waterbird community composition changes (using Principal Components Analysis) associated with decreasing nutrient loading and increasing water transparency at two lakes, with no change in breeding waterbird community at the third. Secchi depth explained 73–95% of variance in both PC axes at both restored lakes, but not at the third, suggesting water transparency was the major factor driving waterbird community composition. These examples show predicting waterbird species-specific responses to management can usefully direct the use of breeding waterbirds as indicator species.

Ecosystem changes in eight Danish lakes after zebra mussel invasion

AbstractInvasion of zebra mussels (Dreissena polymorpha) can have profound effects on lake ecosystems. We investigated the overall ecological effects after invasion and fast expansion of zebra mussels in eight lakes in Denmark belonging to the same watercourse systems but with different morphological and eutrophication characteristics. A few years after the invasion, most of the lakes had experienced profound changes in lake water quality, e.g. chlorophyll a had been reduced by 18–62% and Secchi depth had increased by 38–171%. Nutrient concentrations also decreased, total phosphorus to 37–64% and total nitrogen to 49–77% of pre-invasion concentrations. Seasonally, differences between both summer and winter phosphorus and nitrogen concentrations diminished, suggesting a change in internal nutrient cycling after the invasion. In most lakes, the abundance of submerged macrophytes rose substantially, and often coverage increased by a factor 10 or more, and the maximum colonisation depth increased by up to 5.4 m. Plant species numbers increased markedly in four of the six lakes with macrophyte data. The fish community changed towards higher dominance of potential piscivorous perch and an increase in their body size. The profound effects of zebra mussel invasion may affect lake management and how ecological quality should be interpreted.

Advancing reservoirs water quality parameters estimation using Sentinel-2 and Landsat-8 satellite data with machine learning approaches

Reservoir eutrophication, caused by human activities and climate change, has emerged as a critical environmental concern that has attracted both governmental and public attention. However, accurate measurement of water quality parameters, such as chlorophyll a (CHL-a), water clarity (Secchi depth; SD), and total suspended solids (TSS), in inland waters is challenging due to the optical complexity of individual water bodies, which impedes the optimization of conventional bio-optical algorithms. The aim of this study was to demonstrate the viability of harmonizing Sentinel-2 Multi-Spectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery surface reflectance (SR) products to facilitate the monitoring of inland reservoir CHL-a, SD, and TSS using the Google Earth Engine (GEE) platform and machine learning algorithms. Machine learning models were trained using Landsat-8 OLI and Sentinel-2 MSI surface reflectance products to identify bands and combinations predicting CHL-a, SD, and TSS. Among the machine learning algorithms tested, random forest (RF) (S-2 MSI: R2 = 0.61, mean absolute error [MAE] = 6.56%, root-mean-square error [RMSE] = 12.51 μg/L, L-8 OLI: R2 = 0.56, MAE = 8.44%, RMSE = 16.01 μg/L) yielded the best results in the test set for CHL-a prediction from the Sentinel-2 MSI and Landsat-8 OLI, outperforming the k-nearest neighbor (KNN), AdaBoost, and artificial neural network (ANN) models. It also showed superior performance for SD and TSS prediction. The feature importance analysis revealed that specific band ratios, such as (red/red edge1)*red edge2 for Sentinel-2 MSI and red/blue for Landsat-8 OLI, were significant predictors of CHL-a and TSS. The red/blue band ratio and the green band were highly predictive for SD in Sentinel-2 MSI and Landsat-8 OLI, respectively. The results of fall CHL-a predictions revealed varying trophic levels in the reservoirs. Landsat-8 OLI indicated that 2% of the reservoirs were oligotrophic, while 46%, 43%, and 9% were mesotrophic, eutrophic, and hypertrophic, respectively. Meanwhile, the Sentinel-2 MSI results showed that 51% of the reservoirs were mesotrophic, while 6%, 35%, and 8% were oligotrophic, eutrophic, and hypertrophic, respectively. Overall, this study demonstrates the effectiveness of Landsat-8 OLI and Sentinel-2 MSI surface reflectance products in estimating and monitoring reservoir water quality parameters using machine learning algorithms. This approach has the potential to yield valuable insights aiding the assessment and management of water quality at the regional, national, and global levels.

Ecological condition of marine waters in the area of dredging operations of the kashagan field, Kazakhstan

To determine the hydrophysical and hydrochemical parameters of sea water and their changes during dredging works in the north‐eastern part of the Caspian Sea along the Western Approach Route and near/inside block D, islands EPC‐2, EPC‐3, EPC‐4 and island A of the Kashagan field.Measurement of hydrophysical parameters: salinity, temperature, turbidity, dissolved oxygen concentration, electrical conductivity, pH of seawater was carried out using a portable multi‐parameter analyser "Horiba‐U53" directly in the water column. Water transparency was determined using a Secchi disc. The depth of the water layer was determined using a sounder. Hydrochemical investigations were carried out for seven parameters, with all components sampled in the near‐surface and benthic horizons. Water samples were taken using probes or bathometers: BM‐48, GR‐18 or their analogues.Hydrochemical analyses of seawater samples for: biogens (NO2, NO3, NH4, Ntotal, and Ptotal), synthetic surfactants, phenols, hydrocarbons including polyaromatic hydrocarbons (PAHS), heavy metals, chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). These were carried out in laboratory conditions using analytical equipment according to scientifically accepted methods.The data obtained as a result of the research allow us to draw the following conclusion: changes in the amount of suspended sediment in sea water lead to changes in physical and chemical properties of water (increase in turbidity, disturbance of sediment composition and properties), changes in biotopes, deterioration of oxygen regime, optical properties of water and temperature regime.

Monitoring 36-year water clarity dynamics in turbid waters of the Pearl River Estuary, China, using Landsat data

Water clarity, commonly determined by Secchi disk depth (SDD), is a critical water quality parameter for assessing estuarine ecosystems heath. Since the 1980s, significant changes in SDD have been observed in the Pearl River Estuary (PRE), a robust, economical, and densely populated estuary in southern China. However, its long-term patterns and associated drivers have not yet been systematically investigated. In this study, three novel semi-analytical algorithms suitable for Landsat 5, 7, and 8 images were applied to 36-year (1987–2022) Landsat series data to explored the spatiotemporal heterogeneity of SDD in the PRE. The results indicated that the proposed SDD estimation algorithms yielded well-accepted performances and few errors, with mean absolute percentage errors <27% and root mean square errors <0.105 m. Spatially, the SDDs in the Western regions of the PRE (0.25 ± 0.1 m) were significantly lower than that in the Open areas (0.55 ± 0.15 m). In addition, the SDD showed an overall increasing trend from 1987 to 2022 with a rate of 0.032 m/y over the entire area, indicating gradually improved water quality in the PRE, owing to the less anthropogenic perturbations and landscape change. This research not only produces a 36-year historical SDD dataset for estuarine areas, but also imparts new insights into the complex mechanisms involved in watershed management.

Hydrobiological assessment of two lakes of Jahangirnagar University campus, savar, Bangladesh

The physicochemical and biological parameters of the water in two different lakes on the Jahangirnagar University campus were evaluated between December 2018 and August 2019. The chosen metrics were air and water temperature, Secchi depth, alkalinity, conductivity, pH, total dissolved solids (TDS), dissolved oxygen (DO), soluble reactive phosphate (SRP), soluble reactive silicate (SRS), nitrate-nitrogen (NO3-N) and phytoplankton density (PD). The correlation between all the matrics was done. 37 orders of phytoplankton from lake-1 and 35 orders of phytoplankton from lake-2 were found under 8 classes (Cyanophyceae, Chlorophyceae, Bacillariophyceae, Synurophyceae, Euglenophyceae, Cryptophyceae, Dinophyceae, and Xanthophyceae). According to the principal component Analysis (PCA), 53.8% variation was found among the classes. In addition, Monoraphidium, Oscillatoria, Cosmarium, Actinastrum, Trachlelomonas, Euglena, Pediastrum, Pinnularia, and Synedra were found to be dominant species. It examined the relationships between various environmental factors and dominant phytoplankton species in two lakes and found that the effects of the physicochemical parameters differ depending on the lake and dominant phytoplankton type. The study found negative correlations between various water quality parameters and phytoplankton taxa, suggesting an influence of environmental conditions on phytoplankton communities. Bangladesh J. Bot. 53(1): 159-171, 2024 (March)

Long-term study of phytoplankton dynamics in a supply reservoir reveals signs of trophic state shift linked to changes in hydrodynamics associated with flow management and extreme events

This study analyses over a decade (2009–2022) of monitoring data to understand the impact of hydrological characteristics on water quality and phytoplankton dynamics in Prospect Reservoir, a critical water supply for Greater Sydney, Australia, known for its excellent water quality. Water quality and phytoplankton dynamics were related to hydrodynamics, linked to flow management and the water quality of inflows. Phytoplankton biovolume increased after a prolonged drawdown and subsequent refill event, mainly driven by dinoflagellates, and corresponded to increases in total phosphorus and water temperature. The hydrological period following the 2019/2020 summer bushfires (post-bushfire) that impacted connected reservoirs, was marked by increased flow activity and nutrient loading, leading to significant shifts in the phytoplankton community. Functional group classification and ordination analysis indicated a transition from taxa typically dominant in oligotrophic conditions to meso‑eutrophic. This transition correlated with elevated nutrient levels and chlorophyll-a (Chl-a), and reduced Secchi depth and dissolved oxygen, providing evidence of eutrophication. Q index indicated good water quality post-bushfire, contrasting with a eutrophic status assessment using Chl-a. Our findings highlight the importance of analysing long-term datasets encompassing varied hydroclimatological conditions for a deeper understanding of reservoir behaviour. A comprehensive approach to water quality assessment is recommended, combining functional group classification, Q index and Chl-a measurements for effective reservoir health assessment. This research provides novel insights into the effects of disturbances such as bushfires, on water quality and phytoplankton dynamics in an underrepresented geographic region, offering valuable knowledge for managing water resources amidst growing climate variability.

Patterns of the relationship between the Secchi disk depth and the optical characteristics of water in the Neva Estuary (Baltic Sea): the influence of environmental variables

The Secchi disc depth (Dsd) measurement is widely used to monitor eutrophication and the quality of the aquatic environment. This study aimed to investigate the relationship between Dsd and various factors, including the coefficient of attenuation of photosynthetically active radiation [Kd (PAR)], the depth of the euphotic zone (Deu), PAR at the Secchi disk depth (Esd) and the absorption coefficient of PAR (F) in the Neva Estuary, one of largest estuaries of the Baltic Sea. Environmental variables impacting these indices were identified using data collected from midsummer 2012 to 2020. The Dsd values in the estuary ranged from 0.3 to 4.0 m, with an average value of 1.8 m, while the Deu/Dsd ratio ranged from 1.5 to 6.0 with an average value of 2.8. These values were significantly lower than those observed in the open waters of the Baltic Sea. The highest Deu/Dsd ratio was observed in turbid waters characterized by high Kd(PAR) and low Dsd. Contrary to expectations, Dsd did not exhibit a significant relationship with the concentration of chlorophyll a, raising doubts about the utility of historical Dsd data for reconstructing phytoplankton development in the estuary. Principal component analysis did not identify the primary environmental variables strongly affecting the optical characteristics of water. However, recursive partitioning of the dataset using analysis of variance (CART approach) revealed that the concentration of suspended mineral matter (SMM) was the primary predictor of Deu/Dsd, Kd(PAR), and F. This SMM was associated with the frequent resuspension of bottom sediments during windy weather and construction activities in the estuary. Concentrations of suspended organic matter and the depth of the water area were found to be less significant as environmental variables. Furthermore, the CART approach demonstrated that different combinations of environmental variables in estuarine waters could result in similar optical indicator values. To reliably interpret the data and determine the optical characteristics of water in estuaries from Dsd, more complex models incorporating machine learning and neural connections are required. Additionally, reference determinations of Esd in various regions with specific sets of environmental variables would be valuable for comparative analyses and better understanding of estuarine systems.

Ecological degradation of a fragile semi-arid wetland and the implications in its microbial community: The case study of Las Tablas de Daimiel National Park (Spain)

Las Tablas de Daimiel National Park (TDNP, Iberian Peninsula) is a semi-arid wetland of international significance for waterfowl and serves as a migratory route for various bird species. However, TDNP presents strong anthropization and fluctuating water levels, making it a highly fragile ecosystem. Water physico-chemical parameters and microbial diversity of the three domains (Bacteria-Archaea- Eukarya) were analysed in Zone A and Zone B of the wetland (a total of eight stations) during spring and summer, aiming to determine how seasonal changes influence the water quality, trophic status and ultimately, the microbial community composition. Additionally, Photosynthetically Active Radiation (PAR) was used to determine the trophic status instead of transparency using Secchi disk, setting the threshold to 20–40 μmol/sm2 for benthic vegetation growth. In spring, both zones of the wetland were considered eutrophic, and physico-chemical parameters as well as microbial diversity were similar to other wetlands, with most abundant bacteria affiliated to Actinobacteriota, Cyanobacteria, Bacteroidota, Gammaproteobacteria and Verrumicrobiota. Methane-related taxa like Methanosarcinales and photosynthetic Chlorophyta were respectively the most representative archaeal and eukaryotic groups. In summer, phytoplankton bloom led by an unclassified Cyanobacteria and mainly alga Hydrodictyon was observed in Zone A, resulting in an increase of turbidity, pH, phosphorus, nitrogen, chlorophyll-a and phycocyanin indicating the change to hypertrophic state. Microbial community composition was geographical and seasonal shaped within the wetland as response to changes in trophic status. Archaeal diversity decreases and methane-related species increase due to sediment disturbance driven by fish activity, wind, and substantial water depth reduction. Zone B in summer suffers less seasonal changes, maintaining the eutrophic state and still detecting macrophyte growth in some stations.This study provides a new understanding of the interdomain microbial adaptation following the ecological evolution of the wetland, which is crucial to knowing these systems that are ecological niches with high environmental value.

A variational optimization algorithm for Secchi disk depth based on Multi-satellite data

A Secchi depth is a basic parameter to describe the optical properties of water, which is related to the composition and content of chlorophyll, suspended solids and yellow substances in water, and is also closely related to solar radiation on the surface of water, physical and chemical properties of water and meteorological conditions. The most direct way to obtain the spatiotemporal distribution of water Secchi depth is to use ships to regularly measure the Secchi depth of the base-stations, but this method can only obtain the Secchi depth of the measurement station state, and it is impossible to obtain the Secchi depth characteristics of seawater with large spatiotemporal distribution. As a brand-new observation method, remote sensing technology can obtain the distribution characteristics of ocean parameters in large time and space. In recent years, with the rapid development of remote sensing technology, especially the development of water-colored remote sensors and the improvement of the accuracy of inversion algorithms, many remote sensing products for water Secchi depth have been provided. However, due to the difference in orbit operation and observation parameters of different remote sensing loads, water Secchi depth products are affected by clouds and meteorological climate environment, and there is no same standard for verification and evaluation, which limit the promotion and application of water Secchi depth products. At the same time, data integrated technology has been widely recognized by many disciplines and has been greatly developed in recent years, and pixel-level, feature-level, decision-level integrated technology and development are also popular research directions in the world. Therefore, based on an improved variational optimization algorithm, this article integrates SDD retrieval products on multiple satellite data t such as SNPP, MODIS, and MERIS, and verifies them with ship survey data. The correlation is better than 0.9, proving that this method can improve the accuracy of SDD inversion products.

Contribution of combined stressors on density and gene expression dynamics of the copepod Temora longicornis in the North Sea.

The impact of multiple environmental and anthropogenic stressors on the marine environment remains poorly understood. Therefore, we studied the contribution of environmental variables to the densities and gene expression of the dominant zooplankton species in the Belgian part of the North Sea, the calanoid copepod Temora longicornis. We observed a reduced density of copepods, which were also smaller in size, in samples taken from nearshore locations when compared to those obtained from offshore stations. To assess the factors influencing the population dynamics of this species, we applied generalised additive models. These models allowed us to quantify the relative contribution of temperature, nutrient levels, salinity, turbidity, concentrations of photosynthetic pigments, as well as chemical pollutants such as polychlorinated biphenyls and polycyclic aromatic hydrocarbons (PAHs), on copepod density. Temperature and Secchi depth, a proxy for turbidity, were the most important environmental variables predicting the densities of T. longicornis, followed by summed PAH and chlorophyll concentrations. Analysing gene expression in field-collected adults, we observed significant variation in metabolic and stress-response genes. Temperature correlated significantly with genes involved in proteolytic activities, and encoding heat shock proteins. Yet, concentrations of anthropogenic chemicals did not induce significant differences in the gene expression of genes involved in the copepod's fatty acid metabolism or well-known stress-related genes, such as glutathione transferases or cytochrome P450. Our study highlights the potential of gene expression biomonitoring and underscores the significance of a changing environment in future studies.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were “strong” for Landsat 5 and 7 (rs = 0.78 and 0.79), and “very strong” for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from −0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

Remote monitoring of water clarity in coastal oceans of the Guangdong-Hong Kong-Macao Greater Bay Area, China based on machine learning

The development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and densely populated regions in China, has posed an increasing threat to the health of the water environment in adjacent coastal oceans. However, the spatiotemporal variations of water clarity in the coastal oceans of the GBA (COGBA) have not been well-documented. Therefore, this study aims to develop a remote sensing retrieval algorithm for assessing water clarity, represented by Secchi disk depth (ZSD), based on Landsat-8 OLI imagery for the COGBA using a machine learning method. Cross-validation demonstrated that the algorithm performed exceptionally well, with an R2 value greater than 0.8. By applying the algorithm to 263 Landsat-8 OLI images, we obtained a time series of ZSD for the period of 2013–2021 over the COGBA. Results indicate that the Pearl River Estuary (PRE) exhibited the highest turbidity, followed by the Daya Bay and the Mirs Bay. Generally, ZSD increased from the northwest to the southeast of the COGBA. Seasonal, interannual, and long-term variations in ZSD were also observed with long-term increases in the PRE and the Daya Bay. The interannual variations in ZSD during fall were primarily regulated by different factors in each region. In the PRE, the negative effects of sediment discharge and wind speed played a significant role. In Mirs Bay, wind speed and sediment discharge had a negative impact on ZSD. In Daya Bay, precipitation and wind speed were the key factors influencing ZSD. The development and findings of our algorithm contribute to the protection and management of the water environment in the COGBA, facilitating effective governance measures.

Remote Sensing-Based Water Quality Parameters Retrieval Methods: A Review

Water quality is a sensitive global environmental issue, as it is important for long-term economic development and environmental sustainability. It is a general descriptor of water properties in terms of physical, chemical, thermal, and/or biological characteristics. Laboratory analysis is used to measure and analyze water quality parameter, however, it is a conventional, time-consuming, and expensive approach often providing discrete data at a single point in space and time and making it difficult to characterize a larger waterbody, while remote sensing methods is a cost effective and accurate methods of water quality monitoring with a high spatial and temporal resolution for large area of waterbodies. To this end, this review focused on novel findings in the field of water quality estimation using remote sensing techniques, and the result revealed that remote sensing method has used to retrieve water quality parameters which are optically active (Chlorophyll-a, Secchi Disk depth, Water temperature, Turbidity, Total Suspended Matter, Electrical conductivity, Sea Surface Salinity and Colored Dissolved Organic Matter), and optically non active (Dissolved Oxygen, Chemical Oxygen Demand, Biochemical Oxygen Demand, Total Nitrogen, Ammonia Nitrogen and Total Phosphorus). Various properties (spectral, spatial and temporal, etc.) of the more commonly employed multi spectral and hyper spectral sensors of both satellite and non-satellite-born data sources are tabulated to be used as a sensor selection guide. Furthermore, this paper summarizes the commonly used different retrieval algorisms (analytical, empirical, and artificial intelligence/machine learning (AI/ML)) employed in evaluating and quantifying the water quality parameters. As a whole, remote sensing technology is a permissible method for water quality monitoring across the world in its spatio-temporal coverage, accuracy, and its cost effectiveness

Productividad primaria en un estanque de cría de peces (31°12’S; 57°55’W)

Gross and net primary productivity, respiration and related parameters were estimated in an experimental fish pond where silverside (Basilichthys bonariensis) were bred, using the oxygen light-dark bottle method. The results obtained showed a positive correlation (r = 0.83, p &lt; 0.005) with temperature. The chlorophyll a concentration, the number of phytoplankton cells and the Secchi disk showed no correlation with primary productivity.The mean gross primary productivity on sunny days was 2022 g C m-2 day-1 in the deepest zone, with a 47% variation. For the whole pond the results was 358.7 KgC ha-1 year-1 considering a correction for statistical average of sunny days. The respiration/ gross primary productivity relation was 0.50 for the whole pond.With comparative purposes the primary productivity was estimated with the simplified method of daily curves and by the method of oxygen changes in open waters.

What's good for fish is good for fishes: Interspecific consistency of growth, condition, and abundance among glacial lakes

AbstractObjectiveFish abundance and growth are regulated by a combination of bottom‐up and top‐down forces, but many management techniques depend more heavily on the latter. Here, we evaluated whether intersystem variation in nutrient loading and bottom‐up forces play more dominant roles in the control of abundance and growth of species across similar lakes than intra‐ and interspecific compensatory effects. We aimed to assess whether patterns of abundance and growth are consistent among fish species across lakes.MethodsWe tested this by evaluating pairwise comparisons of catch‐per‐unit‐effort, condition, and length‐at‐age data for 11 common warmwater fish species from 184 mesotrophic and eutrophic glacial lakes in Indiana, United States. We characterized the environmental conditions of each lake using limnological measurements (e.g., chlorophyll‐a concentration and surface temperature), lake morphology descriptions (e.g., depth and size), catchment characteristics (e.g., percent agricultural land cover), and nutrient load modeling using the Long‐Term Hydrologic Impact Analysis model. Taking a meta‐analysis approach, we used effect size calculation from pairwise correlations among lakes to identify environmental and community impacts on species abundance, condition, and length at age.ResultWe demonstrated that there were positive associations among most species comparisons (i.e., multiple species experiencing relatively fast growth in the same lake). Evaluations of environmental conditions among systems suggested that differences in estimated phosphorus input and the limnological measurements of total phosphorus, Secchi depth, and chlorophyll‐a concentration were good predictors of length at age and catch per unit effort for fish.ConclusionOur results indicated that there is a strong and consistent influence of environmental conditions and bottom‐up processes in determining species abundance and growth. This suggests that bottom‐up forces and environmental conditions linked to nutrient loading likely determine the upper boundary of fish abundance and growth in these lakes.

Strategies for regulating the intensity of different cyanobacterial blooms: Insights from the dynamics and stability of bacterioplankton communities

The occurrence of cyanobacterial blooms is increasing in frequency and magnitude due to climate change and human activities, which poses a direct threat to drinking water security. The impacts of abiotic and biotic factors on the development of blooms have been well studied; however, control strategies for different bloom intensities have rarely been explored from the perspective of the dynamics and stability of bacterioplankton communities. Here, a network analysis was used to investigate the interactions and stability of microbial communities during different periods of R. raciborskii bloom in an inland freshwater lake. The abundance and diversity of rare taxa were significantly higher than that of abundant taxa throughout the bloom cycle. At the pre-bloom (PB) stage, microbial interactions among the different bacterial groups were weak but strongly negatively correlated, indicating low robustness and weak disturbance resistance within the community. However, community stability was better, and microbial interactions became more complicated at the high-bloom (HB) and low-bloom (LB) stages. Interestingly, rare taxa were significantly responsible for community stability and connectivity despite their low relative abundance. The Mantel test revealed that Secchi depth (SD), orthophosphate (PO43−-P), and dissolved oxygen (DO) were significantly positively correlated with abundant taxa, rare taxa and PB. DO was significantly positively correlated with HB, intermediate taxa, and rare taxa, while water temperature (WT), N/P and total nitrogen (TN) were significantly positively correlated with LB, abundant taxa, intermediate taxa, and rare taxa. These findings suggest that reducing the PO43−-P concentration at the PB stage may be an effective approach to preventing the development of R. raciborskii blooms, while regulating rare taxa at the HB and LB stages may be a key factor in controlling R. raciborskii blooms.

Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data

AbstractToxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions.

Wide ecological niches ensure frequent harmful dinoflagellate blooms

Harmful algal blooms (HABs) and their consequences cause multiple devastating effects in various freshwater, brackish and marine ecosystems. However, HAB species at moderate population densities have positive ecological roles as primary producers of organic matter and food for zooplankton and fish. They also enhance benthic-pelagic coupling and participate in the biogeochemical cycles. The consequences of HABs are transported across the conventional environmental boundaries by numerous cascade effects in the food webs and beyond. Meanwhile, forecasts of bloom events are still limited, largely because of scarcity of reliable information on ecological niches of the bloom-forming algae. To fill up this knowledge gap, this study focused on dinoflagellates, a diverse group of mostly photosynthesizing protists (unicellular eukaryotes) capable of mixotrophy, since they play a key role in primary production and formation of blooms in marine and brackish waters worldwide. In this study, ecological niches of 17 abundant bloom-forming dinoflagellate species from coastal regions of the southern Baltic Sea were identified for the first time. It was hypothesized that wider ecological niches ensure more frequent dinoflagellate blooms compared to the species with narrower niches. This hypothesis was verified using the long-term (44 years) database on phytoplankton abundance and physical-chemical characteristics of the environment. It were analyzed 4534 datasets collected from 1972 to 2016. Fourteen abiotic parameters (water temperature, salinity, Secchi depth, pH, Chl a, and concentration of basic nutrients) were considered as ecological niche dimensions. The Principal Component Analysis presented the dissolved inorganic nitrogen, total nitrogen, Chl a, and temperature as principal niche dimensions of dinoflagellates. The algal bloom criteria were refined. It was for the first time proved statistically that HAB frequency of dinoflagellate species robustly correlated with the width of their ecological niches.

Comparison of the zooplankton-based RCC to Carlson’s trophic state indices and water quality parameters

Zooplankton is not included as a biological quality element, even though it is a key component of pelagic food webs. The data on zooplankton community structure can be used to evaluate the trophic state of lakes through an RCC Index, based on quantitative proportions between the main zooplankton groups. As previous investigations have related the index only to the Secchi disc transparency, the aim of this study was to compare the Index to a set of water quality parameters and trophic state indices. Two sandpit lakes with contrasting characteristics were selected as model lakes for the study. A total of 57 samples were collected simultaneously with the physicochemical water quality parameters between 2016 and 2020. Carlson’s trophic state indices were used to assess the trophic state of the lakes. Pearson correlation coefficient and Principal Component Analysis were used to assess the observed correlations between the measured parameters and to distinguish what factors regulate zooplankton community structure. Overall, 86 zooplankton taxa were identified. RCC Index ranged from 2 in the small sandpit lake to 87 in the big sandpit lake. A highly significant negative correlation was found between the RCC Index and the values of TP and COD. The results complement the arguments for the inclusion of zooplankton as a BQE for the assessment of the status of lakes.