Biological Early Warning System Research Articles

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

Biological early warning system (BEWS) has been globally used for surface water quality monitoring. Despite its extensive use, BEWS has exhibited limitations, including difficulties in biological interpretation and low alarm reproducibility. This study addressed these issues by applying machine learning (ML) models to eight years of in-situ BEWS data for Daphnia magna. Six ML models were adopted to predict contamination alarms from Daphnia behavioral parameters. The light gradient boosting machine model demonstrated the most significant improvement in predicting alarms from Daphnia behaviors. Compared with the traditional BEWS alarm index, the ML model enhanced the precision and recall by 29.50% and 43.41%, respectively. The speed distribution index and swimming speed were significant parameters for predicting water quality warnings. The nonlinear relationships between the monitored Daphnia behaviors and water physicochemical water quality parameters (i.e., flow rate, Chlorophyll-a concentration, water temperature, and conductivity) were identified by ML models for simulating Daphnia behavior based on the water contaminants. These findings suggest that ML models have the potential to establish a robust framework for advancing the predictive capabilities of BEWS, providing a promising avenue for real-time and accurate assessment of water quality. Thereby, it can contribute to more proactive and effective water quality management strategies.

Application of biological early warning systems in wastewater treatment plants: Introducing a promising approach to monitor changing wastewater composition

Wastewater treatment plants (WWTPs) are a major source of micropollutants to surface waters. Currently, their chemical or biological monitoring is realized by using grab or composite samples, which provides only snapshots of the current wastewater composition. Especially in WWTPs with industrial input, the wastewater composition can be highly variable and a continuous assessment would be advantageous, but very labor and cost intensive. A promising concept are automated real-time biological early warning systems (BEWS), where living organisms are constantly exposed to the water and an alarm is triggered if the organism's responses exceed a harmful threshold of acute toxicity. Currently, BEWS are established for drinking water and surface water but are seldom applied to monitor wastewater. This study demonstrates that a battery of BEWS using algae (Chlorella vulgaris in the Algae Toximeter, bbe Moldaenke), water flea (Daphnia magna in the DaphTox II, bbe Moldaenke) and gammarids (Gammarus pulex in the Sensaguard, REMONDIS Aqua) can be adapted for wastewater surveillance. For continuous low-maintenance operation, a back-washable membrane filtration system is indispensable for adequate preparation of treated wastewater. Only minor deviations in the reaction of the organisms towards treated and filtered wastewater compared to surface waters were detected. After spiking treated wastewater with two concentrations of the model compounds diuron, chlorpyrifos methyl, and sertraline, the organisms in the different BEWS showed clear responses depending on the respective compound, concentration and mode of action. Immediate effects on photosynthetic activity of algae were detected for diuron exposure, and strong behavioral changes in water flea and gammarids after exposure to chlorpyrifos methyl or sertraline were observed, which triggered automated alarms. Different types of data analysis were applied to extract more information out of the specific behavioral traits, than only provided by the vendors algorithms. To investigate, whether behavioral movement changes can be linked to impact other endpoints, the effects on feeding activity of G. pulex were evaluated and results indicated significant differences between the exposures. Overall, these findings provide an important basis indicating that BEWS have the potential to act as alarm systems for pollution events in the wastewater sector.

Non-Linear Analyses of Fish Behaviours in Response to Aquatic Environmental Pollutants—A Review

Analysis of fish behaviour is an effective way to indirectly identify the presence of environmental pollutants that negatively affect fish life, its production and quality. Monitoring individual and collective behaviours produces large amounts of non-linear data that require tailor-suited computational methods to interpret and manage the information. Fractal dimension (FD) and entropy are two groups of such non-linear analysing methods that serve as indicators of the complexity (FD) and predictability (entropy) of the behaviours. Since behavioural complexity and predictability may be modulated by contaminants, the changes in its FD and entropy values have a clear potential to be embedded in a biological early warning system (BEWS), which may be particularly useful in Precision Fish Farming settings and to monitor wild populations. This work presents a review of the effects of a wide range of environmental contaminants, including toxic compounds, cleaning and disinfecting agents, stimulant (caffeine), anaesthetics and antibiotics, heavy metals (lead, cupper, and mercury), selenium, pesticides and persistent environmental pollutants, on the FD and entropy values of collective and individual behavioural responses of different fish species. All the revised studies demonstrate the usefulness of both FD and entropy to indicate the presence of pollutants and underline the need to consider early changes in the trend of the evolution of their values prior to them becoming significantly different from the control values, i.e., while it is still possible to identify the contaminant and preserve the health and integrity of the fish.

Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

The use of bivalve mollusks as bioindicators in automated monitoring systems can provide real-time detection of emergency situations associated with the pollution of aquatic environments. The behavioral reactions of Unio pictorum (Linnaeus, 1758) were employed in the development of a comprehensive automated monitoring system for aquatic environments by the authors. The study used experimental data obtained by an automated system from the Chernaya River in the Sevastopol region of the Crimean Peninsula. Four traditional unsupervised machine learning techniques were implemented to detect emergency signals in the activity of bivalves: elliptic envelope, isolation forest (iForest), one-class support vector machine (SVM), and local outlier factor (LOF). The results showed that the use of the elliptic envelope, iForest, and LOF methods with proper hyperparameter tuning can detect anomalies in mollusk activity data without false alarms, with an F1 score of 1. A comparison of anomaly detection times revealed that the iForest method is the most efficient. These findings demonstrate the potential of using bivalve mollusks as bioindicators in automated monitoring systems for the early detection of pollution in aquatic environments.

Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

Anomaly Detection in Biological Early Warning Systems Using Unsupervised Machine Learning

Behavioral Responses in Zebrafish (Danio rerio) Exposed to Heavy Metal and Insecticide Induced Stress

Background: Changes in fish behavior can help identify accidental chemical pollution. Heavy metals and pesticides are two of the most found pollutants to investigate the different behavioral responses of fish to these two types of pollutants exposure. Methods: Real-time computer imaging was utilized to record parameters of fish behaviors, including swimming speed, turning frequency, depth and distance between fish. Deltamethrin and cadmium were 0.015 ppm and 3.5 ppm, respectively. It was conducted for a total period of 180 min. Fish behaviors were recorded with dechlorinated water during the first 60 mins, then deltamethrin and cadmium was introduced to observe behavioral responses of zebrafish during the next 120 mins. Result: As a result of increased swimming activity, the first response of zebrafish is avoidance followed by a changed distribution in the test chamber. The duration of hyperactivity during deltamethrin exposure was lasted 35 minutes larger than Cd exposure and the average swimming depth showed totally different trends with increased from 140 mm to 226 mm during deltamethrin exposure but decreased from 161 to 84 mm during cadmium exposure. It is proved that these different responses do exist under in the two chemicals studied and this may contribute to the development of biological early warning system to separate accidental chemical pollution types.

Biological Early Warning Systems: The Experience in the Gran Sasso-Sirente Aquifer

Biological early warning systems (BEWS) are installed worldwide for the continuous control of water intended for multiple uses. Sentinel aquatic organisms can alert us to contaminant presence through their physiological or behavioural alterations. The present study is aimed at sharing the experience acquired with water biomonitoring of the Gran Sasso-Sirente (GS-S) aquifer. It represents the major source of the Abruzzi region surface water, also intended as drinkable and for irrigation use. Besides the biomonitoring of drinkable water of the Teramo Province made by Daphnia Toximeter and irrigation water of the L’Aquila Province by Algae Toximeter, a novel sensor named “SmartShell” has been developed to register the behaviour of the “pea clam” P. casertanum, an autochthonous small bivalve living in the Nature 2000 site “Tirino River spring”. The valve movements have been recorded directly on the field. Its behavioural rhythms have been analysed through spectral analyses, providing the basis for further investigations on their alterations as early warnings and allowing us to propose this autochthonous bivalve species as a novel sentinel organism for spring water.

Environmental control: from inertia of the standard biomonitoring to speed of the online one

Standard biomonitoring system based on sampling technology is not capable of efficient environmental control and ecosystem protection from anthropogenic impacts and followed degradation because of its extremely delayed response to any environmental changes and pollution first of all. Contemporary approach to ecological biomonitoring turns to on-line control of environmental safety. New online technologies and systems of ecological control based on monitoring of the activity parameters in some species-biosensors have been intensively introduced in many countries since 90s as biological early warning systems (BEWS) such as Musselmonitor. The next step is the development of up-to-date technology from simple BEWS to online biosensor monitoring (OBM) in natural ecosystems as a promising tool for immediate detecting of environmental changes and aquatic toxicity in particular. The first technology of online ecological monitoring has been developed in MMBI (1985-2016) from basic to applied research. It was established, that to obtain the reliable OBM technology the background behavior must be properly investigated for long time prior to online-biomonitoring implementation in the field. Nowadays the main task for online biosensor monitoring is the bioindication technology for recognizing of any harm pollution effects online in any time span.

Chemicals Weaken Shoal Preference in the Rare Minnow Gobiocypris rarus

Fish behavioral responses are sensitive to chemicals in the water. We tested rare minnow tested for their shoal preference, and the shoal (school) factors including nutritional status, body size, and shoal (school) size that can make their preference most stable were measured. Then shoal preference was measured again while fish and shoal were subjected to a concentration gradient of chemicals (cadmium ion [Cd2+ ], tricaine methanesulfonate [MS222], and p-chloroaniline). The results showed that single rare minnow preferred shoals over blank control tanks. In addition, this preference was most stable when the shoal was well fed and contained 20 individuals 2 cm long. Although there was no significant response after exposure to p-chloroaniline, the time spent from entering the tank to start moving decreased greatly at concentrations of Cd2+ >3 mg/L and MS222 >11 mg/L. The time the test fish spent close to the shoal significantly decreased at Cd2+ >3 mg/L, MS222 >11 mg/L, and p-chloroaniline >10 mg/L, and the frequency of boundary line crossing increased at the same concentrations. The behavioral parameters changed by 20, 5, and 8 min once the lowest-observed-effect concentrations of Cd2+ , MS222, and p-chloroaniline, respectively, were added. Our study provides useful information on rare minnow shoal preference that may be used for a biological early warning system. Environ Toxicol Chem 2020;39:2018-2027. © 2020 SETAC.

Daphnia magna metabolic profiling as a promising water quality parameter for the biological early warning system

The inclusion of omics data into water quality monitoring programs is being considered to help alleviate the growing threat to water resources and ecosystem services. Despite the increasing need, the biological early warning system (BEWS), the widely used real-time water quality monitoring system, does not currently incorporate omics information, despite that metabolomics is a highly sensitive indicator of organism health and stress. We examined Daphnia magna metabolomics, which is the analysis of small molecules in living D. magna, as a potential water quality parameter for incorporation in the BEWS. The concentrations of 24 metabolites were measured with changes in water quality and variation of metabolite abundances was compared within and between conditions. Age-dependent monitoring revealed that matured individuals older than 8 days are appropriate model organisms for monitoring based on their low metabolomic variation as compared to younger daphnids. Hourly monitoring of metabolic variability and regulation under ambient and starved conditions demonstrated the rapid and sensitive detection of nutritional changes. Moreover, the metabolomic dysregulation due to exposure to the pollutant propranolol was also observed. By integrating all the observations, we found that the D. magna metabolome is a sensitive and useful parameter for detecting water quality changes and how these alter the function of keystone organisms. As such, this metabolomics-based framework is applicable to BEWS and highlights the beneficial advantages of integrating biomolecular and apical endpoint observations for enhanced performance in biomonitoring programs.

Zebrafish neurobehavioral phenomics applied as the behavioral warning methods for fingerprinting endocrine disrupting effect by lead exposure at environmentally relevant level

Environmental lead (Pb) exposure is a great hazard to the public health. Although environmentally relevant Pb poisoning is preventable, insidious Pb contaminants are still a major threat to human health. Herein, we reported that exposure to Pb at environmentally relevant concentration level (1 μg/L, 10 μg/L and 100 μg/L), disturbed the courtship behavior of adult male zebrafish and further altered the transcriptional patterns of key genes involved in testicular steroidogenesis (igf3, amh, piwil1, lhcgr, fshr, cyp11c1, star, cyp19a1a, cyp19a1b) and apoptosis (bax, cytoC, caspase 9, caspase 3, puma). Both the behavioral and the transcriptional profiles share a similar biphasic dose response, with stimulatory effects after low-level exposure and inhibitory effects after high-level exposure. This results revealed the endocrine disrupting effects of Pb even at an environmentally relevant level within the concentration range of ambient water quality criteria (AWQC) and the reliability of locomotion fingerprint as the indicator for detecting the risk induced by Pb pollution. Current research, for the first time, employed the ZebraTower system as the biological early warning system (BEWS) to find that Pb exerted biphasic effects on the courtship behavior and endocrine regulation of male adult zebrafish. Methodologically, we firstly propose an efficient solution to monitor and assess the risk of Pb exposure by combining the (BEWS) and data analyzing methods such as zebrafish phenomics, which would make a contribution to the detection and prevention of environmentally relevant Pb poisoning.

Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress.

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

Evaluation of the sensitivity spectrum of a video tracking system with zebrafish (Danio rerio) exposed to five different toxicants.

The aim of this study was to develop a biological early warning system for the detection of aquatic toxicity and test it with five toxicants with distinct chemical nature. This was done in order to verify the spectrum of sensitivities of the proposed system, as well as the potential identification capability of the tested contaminants, using only the analysis of zebrafish's behavior. Six experimental conditions were tested: negative control and five toxicants (bleach, lindane, tributyltin, mercury, and formaldehyde). The exposure time was 45min, and the concentrations used corresponded to 9% of LC50's-96h for the tested compounds, to ensure ecologically relevant results. A total of 108 fish were used, with each individual experimental condition being tested 18 times. A statistical model of diagnosis was used, combining self-organizing map and correspondence analysis. The values of sensitivity, specificity, accuracy, false positive, false negative, positive predictive value (PPV), and negative predictive value (NPV) were calculated. The objectives of the work were accomplished and the system showed a good overall diagnostic performance with 79% in accuracy, 77% in sensitivity, and 88% in specificity. The lowest result of the predictive values was 78% (lindane and mercury), in the case of the NPV, and 86% (bleach and lindane), in the case of the PPV. The best result of the predictive values was 100% (bleach and tributyltin), for the NPV, and 89% (tributyltin), for the PPV. Regarding the five tested toxicants, the system was able to correctly identify the agent responsible for the contamination in 40% of the positive diagnoses.

Biomonitoring of coastal marine waters subject to anthropogenic use: development and application of the biosensor Mosselmonitor®

Abstract The overall objective of this study was to develop a biological early warning system (Mosselmonitor®) on offshore platform to detect critical environmental situations. The experiment was conducted on oil off-shore platform called Rospo Mare B. This structure is located in the area in front of Molise coast line (Italy, Adriatic Sea), characterized by a depth of about 77 m and a bathymetry between 65 and 80 m. The Mosselmonitor® works with eight mussels connected via specific sensors to PC for recording opening values of valves. A probe was installed inside the instrument to daily control of water pH, dissolved oxygen, salinity and temperature. Water samples are weekly analyzed for heavy metals, organochlorine pesticide and suspended matter. During the entire observation period, closure alarms were predominantly detected (99.9%) and a decrease of 65% in alarms maximum duration was recorded from the fifth week. During the first month, none changes in water physico-chemical parameters were observed so that affect the bivalves behavior. The only chemical parameter steadily detected in water was copper; its average concentrations were of 10 ppb. Detected alarms were not comparable to those recorded in the first month: this observation could be explained considering that mussels will be adapted to copper constant presence.

Stress test of a biological early warning system with zebrafish (Danio rerio).

The aim of this work was to develop a novel methodology to stress test the diagnostic capability of a video tracking system with zebrafish (Danio rerio), against two pre-established disturbances. Eight different treatments were tested varying the presence or absence of a toxicant (NaOCl) and two disturbances: the passing of a shadow (mimicking a predator) and entrapment of the fish. The concentration tested corresponded to a sublethal (1 % 24 h-LC50) and short term exposure (2 h). A total of 56 organisms were tested resulting in 112 diagnoses (before and after the contamination). A statistical model of diagnosis was developed using Self-organizing Map (SOM) and Correspondence Analysis (CA). Sensitivity, specificity, accuracy, false positive and false negative values were calculated to evaluate the diagnostic performance. The disturbances did not negatively affect the capability of the model. In the presence of at least one of these variables, the diagnostic performance was similar or even superior to the baseline results without disturbances. Furthermore, the system produced a large number of correct diagnoses, at an ecologically relevant concentration of exposure, in a non-invasive way.

A sensitive biomarker for the detection of aquatic contamination based on behavioral assays using zebrafish larvae

An effective biological early warning system for the detection of water contamination should employ undemanding species that rapidly react to the presence of contaminants in their environment. The demonstrated reaction should be comprehensible and unambiguously evidential of the contamination event. This study utilized 96h post fertilization zebrafish larvae and tested their behavioral response to acute exposure to low concentrations of cadmium chloride (CdCl2) (5.0, 2.5, 1.25, 0.625mg/L) and permethrin (0.05, 0.029, 0.017, 0.01μg/L). We hypothesize that the number of larvae that show advanced trajectories in a group corresponds with water contamination, as the latter triggers avoidance behavior in the organisms. The proportion of advanced trajectories in the control and treated groups during the first minute of darkness was designated as a segregation parameter. It was parametrized and a threshold value was set using one CdCl2 trial and then applied to the remaining CdCl2 and permethrin replicates. For all cases, the method allowed distinguishing between the control and treated groups within two cycles of light: dark. The calculated parameter was statistically significantly different between the treated and control groups, except for the lowest CdCl2 concentration (0.625mg/L) in one replicate. This proof-of-concept study shows the potential of the proposed methodology for utilization as part of a multispecies biomonitoring system.

Resilience assessment of a biological early warning system based on the locomotor behavior of zebrafish (Danio rerio).

With the development of new tools such as biological early warning systems, it becomes extremely important to test their reliability and detection capability. This work aimed at testing the sturdiness of a video tracking system by determining whether the detection capability does not deteriorate over time, after successive exposures of the zebrafish to three different toxicants, namely sodium hypochlorite, bisphenol A, and ethanol. Zebrafish were exposed to the three tested compounds separately (one fish, one toxicant) once a day, for 1h and 30m over 9days, to 9% of the 96h LC50 of the respective toxicant. The behavior analysis was based on nine movement descriptor parameters of the fish, namely: angular velocity; linear velocity; spatial dispersion; linear acceleration; and angular acceleration. A statistical method was developed using self-organizing map (SOM), correspondence analysis, and linear and orthogonal multiple regression models. The results indicated that the system was able to successfully detect the three toxicants. With ethanol, the detection capability was maintained, but in the case of the sodium hypochlorite and bisphenol A, a deterioration of the detection capability occurred over the 9days. This effect may be due to the induction of detoxification mechanisms and physiological acclimation, or due to the accumulation of adverse effects caused by the repeated exposure to the toxicants. Future works, especially those focusing on the application of similar early warning systems in real-world scenarios, should regularly exchange the sentinel organisms, to avoid degradation of the detection capability, as verified with two of the three tested compounds.

24/24 Smart Stations for Pollution Monitoring Sea Coast or Artificial River

This is a new applied engineering study for the possibility to monitor huge distances of the sea coast, or artificial river collecting water nodes against pollution in water quality by measuring network with a biological early warning system, it is a continuous and automated sea/water quality monitoring and thus makes an important contribution to the early detection of disasters or accidents as well as to the evaluation of hazard potentials. This system is used for efficient control of automated sea/water quality, and continuous sea/water quality monitoring for twenty-four hours’ analysis using smart stations connected over a wireless computer network instead of the old method of sample collection and then lab. Analysis. The functionality of the 100% web-based multi-tier system comprises workflows for automatic sampling and quality assurance, the administration and control of equipment in the measuring stations, numerous options for data evaluation, illustration, and export as well as a notification system.

The valve movement response of three freshwater mussels Corbicula fluminea Müller 1774, Hyriopsis cumingii Lea 1852, and Anodonta woodiana Lea 1834 exposed to copper

Biological early warning system (BEWS) is an innovative system for real-time water quality monitoring based on different behavioral responses of aquatic organisms. Mussels easily meet the requirement for optimal organisms in BEWS. However, little emphasis has been placed on freshwater mussels. In this study, the Hall element sensor system has been used to investigate exposure–response relationships of valve movement in freshwater Corbicula fluminea, Hyriopsis cumingii, and Anodonta woodiana exposed to a copper (Cu2+) gradient of 0, 0.01, 0.1, 1, and 10 mg l−1. The measured behavioral parameters were compared to determine if a response could be elucidated, including the amplitude of valve opening (AVO) and frequency of valve adduction (FVA). The results suggest that the mean AVO was significantly decreased in C. fluminea exposed to 0.1 and 1 mg l−1, and decreased for H. cumingii and A. woodiana after exposure to 10 mg l−1. The mean FVA was significantly decreased in H. cumingii exposed to 1 and 10 mg l−1, while A. woodiana revealed lower frequencies only at 10 mg l−1. The three species showed inherent rhythms of valve movements and dose-dependent responses upon copper exposure, and H. cumingii showed the most obvious profile of a copper dose-valve movement response.

Exploration of Daphnia behavioral effect profiles induced by a broad range of toxicants with different modes of action.

Behavior is increasingly reported as a sensitive and early indicator of toxicant stress in aquatic organisms. However, the systematic understanding of behavioral effects and comparisons between effect profiles is hampered because the available studies are limited to few chemicals and differ in the exposure conditions and effect parameters examined. The aims of the present study were 1) to explore behavioral responses of Daphnia magna exposed to different toxicants, 2) to compare behavioral effect profiles with regard to chemical modes of action, and 3) to determine the sensitivity and response time of behavioral parameters in a new multi-cell exposure system named Multi-DaphTrack compared with currently utilized tests. Twelve compounds covering different modes of toxic action were selected to sample a wide range of potential effect profiles. Acute standard immobilization tests and 48 h of behavioral tracking were performed in the customized Multi-DaphTrack system and a single-cell commercialized biological early warning system. Contrasting behavioral profiles were observed for average speed (i.e., intensity, time of effect onset, effect duration), but no distinct behavioral profiles could be drawn from the chemical mode of action. Most compounds tested in the Multi-DaphTrack system induced an early and significant average speed increase at concentrations near or below the 10% effective concentration (48 h) of the acute immobilization test, demonstrating that the Multi-DaphTrack system is fast and sensitive. To conclude, behavior endpoints could be used as an alternative or complement to the current acute standard test or chemical analysis for the predictive evaluation of ecotoxic effects of effluents or water bodies.