Fish Feeding Behavior Research Articles

Feature fusion strategy and improved GhostNet for accurate recognition of fish feeding behavior

In aquaculture, accurate detection of fish feeding intensity is a critical step for establishing an on-demand feeding system. In this paper, a novel fish feeding intensity detection method based on the fusion of multiple features (Mel spectrogram, STFT, and CQT feature map) and the LC-GhostNet lightweight network was proposed as an experimental object with Oplegnathus. First, a dataset of feeding sounds of Oplegnathus punctatus was built, in which there are four types: “strong”, “medium”, “weak” and “none”. Next, the Mel, STFT, and CQT feature maps of the feeding sound were extracted using the Librosa library, and then these feature maps were fused by feature image stitching. Lastly, the fused audio feature maps were fed to LC-GhostNet for further feature extraction and classification. Experimental results indicated that the accuracy of the fused feature maps was 97.941% when used as input, which was 4.053%, 7.207%, and 3.003% higher than that of the single feature Mel, STFT, and CQT, respectively. The method proposed in this paper is more lightweight and effective than the mainstream methods, and the accuracy has been improved. Based on this automatic and non-destructive method of obtaining fish feeding information, feeding decisions can be effectively optimized.

Occurrence of rare earth elements in water, sediment, and freshwater fish of diverse trophic levels and feeding ecology: Insights from the Po river (northwest Italy)

To date, the occurrence of rare earth elements (REEs) in freshwater ecosystems has garnered limited attention in the scientific literature. Furthermore, a dearth of data exists regarding their potential bioaccumulation in freshwater fish. To fill this knowledge gap, we studied REEs concentrations in water, sediment, and fish specimens collected along the Po River (northwest Italy) and calculated biota-sediment accumulation (BSAF) and bioconcentration (BCF) factors, while taking into account fish feeding behavior and trophic level effects on the overall content of total REEs (ƩREEs). The fish communities were composed of native and non-native species. Remarkably low concentrations of REEs (<0.0003 mg/L) were detected in the water samples, indicating REEs insolubility. In contrast, sediment samples were found to be a good sink for REEs, with a higher mean ƩREEs recorded for the samples from the Moncalieri station (70.93 mg/kg). Notably, no significant differences in ƩREEs concentration were observed in the muscle tissue of fish samples from the three stations. The highest mean ƩREEs was recorded in the samples from the Murazzi station (0.027 mg/kg). The BSAF was very low, consistently below the unit, indicating an absence of bioaccumulation in fish muscle from sediment. In contrast, the BCF was high for several REEs, mainly for Sc and Y. While feeding ecology did not appear to affect REEs accumulation in muscle, there was a significant negative relationship between the trophic level and ΣREEs, indicating a trophic dilution of REEs from predator (Silurus glanis) to planktivorous (Alburnus arborella) fish. This study provides baseline concentrations, trophic transfers, and patterns of REEs in a river system. Further studies are needed to understand the transfer of REEs to other biotic components of lotic ecosystems.

Isogenic lines of rainbow trout (Oncorhynchus mykiss) as a tool to assess how growth and feeding behaviour are correlated to feed efficiency in fish

Accurately measuring individual feed intake is required to include feed efficiency (FE) as an objective in commercial breeding programs. Phenotyping individual feed intake through direct measurements remains complex in fish reared in groups. One way to overcome this challenge is to find proxies for estimating FE. This study aimed to investigate the correlations between fish FE and potential predictive criteria in rainbow trout Oncorhynchus mykiss. As predictive criteria, we considered the variations of body weight assessed as thermal growth coefficients (TGC) and the feeding behaviour assessed as the number of feed demands over a period and the proportion of the demands made before noon. Feed intake was assessed over three different periods in ten isogenic lines allowing a recording for each of the ten genotypes while keeping fish in groups. The protocol consisted of two successive feed deprivation and refeeding phases after initial basal growth periods. Correlations were then calculated between FE, measured either as feed conversion ratio (FCR) or residual feed intake (RFI), and the different indirect criteria. We found positive phenotypic correlations between FCR and RFI over the feed intake measurement periods (r ⩾ 0.79, P < 0.001). Assessment of the relationship between FE traits (FCR and RFI) calculated over the three measurement periods and TGC revealed no significant association. Regarding feeding behaviour, significant differences in the daily profile of feed demands and their numbers were found among the ten isogenic lines. RFI calculated from the first feed intake measurement period and feeding behaviour, assessed as the number of feed demands were positively correlated (r = 0.42–0.49, P ⩽ 0.022). Nevertheless, such correlations were not established for the two other measurement periods following feed deprivation. While we demonstrated that the weight variations during feed deprivation are not a good proxy for FE (FCR or RFI) in rainbow trout, we also highlighted the interest in exploring more the relationships between FE and feeding behaviour in fish.

Mathematical modeling applied to fish feeding behavior

Mathematical modeling applied to fish feeding behavior

A BlendMask-VoVNetV2 method for quantifying fish school feeding behavior in industrial aquaculture

Quantification of fish feeding behavior from an image is crucial for achieving smart feeding in industrial aquaculture. Because fish images provide a wealth of spatial information about their behavior, which can be used to determine the fish feeding intensity. However, most studies only use a single spatial feature to quantify fish feeding behavior. For the extraction of multiple spatial feature indicators, a computational approach is lacking due to image challenges caused by occlusion, overlapping, and clustering during the feeding stage. In this paper, a novel emerging BlendMask-VoNetV2 method is developed to segment two-class fish and distinguish different instance individuals for extracting multiple spatial features. Serial indicators are proposed for analyzing spatial feature variations from the time-series-based videos, such as the number of fish, the number of pixels, and the distance between individual fish. Additionally, we present the first fish dataset with fish occlusion and aggregation for feeding image segmentation in industrial aquaculture. It contains 1038 images consisting of 67,519 instance individuals with pixel annotations for two semantic categories: fish1 (non-occlusion and non-aggregation), and fish2 (occlusion or aggregation). Extensive experiments demonstrate that BlendMask-VoVNetV2 achieves competitive segmentation performance with an accuracy of 83.7% on the feeding dataset, outperforming other instance segmentation algorithms such as SOLOV2, SOTR, ConInst, Mask RCNN.et.al. A distinctive advantage of our idea proposed is beneficial to deal with the problem of inaccurate segmentation caused by severe occlusion and overlapping fish. Finally, the BlendMask-VoVNetV2 method is verified on four videos with non-feeding, strong feeding, medium feeding, and weak feeding. The results show that the method we proposed is effective, which can accurately and objectively depict each moment of the entire feeding process using multiple spatial feature indicators.

Tilapia feeding decision system based on adaptive neuro-fuzzy inference

In industrial recirculating aquaculture, the feed required by fish accounts for a major part of the total expenditure. In this paper, a multi-factor decision making system based on aggregation FIFFB of fish feeding behavior, water temperature T of environmental factors and biomass weight W was proposed to solve the problem of feed waste under the traditional mode. To verify the performance of this model, a fuzzy inference FIS model is constructed for comparison. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) between the predicted and actual feeding amount of ANFIS triplet are 0.78 and 0.19, respectively, which are much lower than the FIS model, and this model is more suitable for predicting the feeding amount decision. At the same time, growth parameters such as WGR, FMAE, K and FCR were compared. The fish growth specifications were fatter and the economic benefits were higher, and the feed conversion rate was increased by 12.35%. Therefore, the triplet ANFIS feeding prediction and decision-making system based on fish aggregation degree, water temperature and body weight is effective and has guiding significance for the precise feeding design of unmanned aquaculture.

An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images

Smart feeding is essential for maximizing resource utilization, enhancing fish growth and welfare, and reducing environmental impact in intensive aquaculture. The image segmentation technique facilitates fish feeding behavior analysis to achieve quantitative decision making in smart feeding. Existing studies have largely focused on single-category object segmentation, ignoring issues like occlusion, overlap, and aggregation amongst individual fish in the fish feeding process. To address the above challenges, this paper presents research on fish school feeding behavior quantification and analysis using a semantic segmentation algorithm. We propose the use of the fish school feeding segmentation method (FSFS-Net), together with the shuffle polarized self-attention (SPSA) and lightweight multi-scale module (LMSM), to achieve two-class pixel-wise classification in fish feeding images. Specifically, the SPSA method proposed is designed to extract long-range dependencies between features in an image. Moreover, the use of LMSM techniques is proposed in order to learn contextual semantic information by expanding the receptive field to extract multi-scale features. The extensive experimental results demonstrate that the proposed method outperforms several state-of-the-art semantic segmentation methods such as U-Net, SegNet, FCN, DeepLab v3 plus, GCN, HRNet-w48, DDRNet, LinkNet, BiSeNet v2, DANet, and CCNet, achieving competitive performance and computational efficiency without data augmentation. It has a 79.62% mIoU score on annotated fish feeding datasets. Finally, a feeding video with 3 min clip is tested, and two index parameters are extracted to analyze the feeding intensity of the fish. Therefore, our proposed method and dataset provide promising opportunities for the urther analysis of fish school feeding behavior.

MSIF-MobileNetV3: An improved MobileNetV3 based on multi-scale information fusion for fish feeding behavior analysis

Assessing the intensity of fish feeding activity using fish feeding behavior can help farmers efficiently decide on the amount of feeding bait. However, accurate extraction of fish feeding behavior features is difficult because of the small area of fish in the image and the randomness of fish swimming. To address this problem, an improved MobileNetV3 network, namely multi-scale information fusion (MSIF)-MobileNetV3, was proposed for analyzing the fish feeding behavior. Specifically, MSIF is a novel channel attention module used to replace the Squeeze-and-Excitation (SE) module that improves the attention of the model to fish schools behavior in feeding images using spatial information integration and multi-scale feature fusion. To evaluate the effectiveness of the proposed method, its performance was compared with that of the MobileNetV3 network optimized using multiple training strategies and other classical convolutional neural networks. It was trained and tested using a self-built dataset, and the experimental results showed that the MSIF-MobileNetV3 network using a basic training strategy obtained an optimal classification accuracy of 96.4 % on the test set. Thus, by analyzing the feeding activity of fish, the proposed method can assist in the automatic selection of bait feed under factory farming conditions.

Food odors alter behavioral responses and olfactory receptors expression in grass carp (Ctenopharyngodon idellus)

Olfaction, the sense of smell, is crucial for animal survival, especially in localizing prey or food. However, little is known about the fish behavioral responses to specific food odors and which olfactory receptor might be activated by the food odors. Here, we examined the evolutionary relationships and classified the subfamilies of olfactory receptors (ORs), and conducted behavioral and gene expression analysis in grass carp (Ctenopharyngodon idellus). We identified 196 intact ORs in the grass carp genome and classified them into 8 subfamilies: β, δ, ε, η, γ, κ, θ, and ζ. 12 representative ORs (4 β, 4 δ, and 4 ε) were highly expressed in the olfactory epithelium (OE) of 24-month-old grass carp. Behavioral exploration found that grass carp showed different behaviors to different food odors at a series of concentrations. Specifically, 0.01 μmol/L amino acid mixtures and 0.005 × Hydrilla verticillata extracts strongly changed the preference index and swimming trajectories in 6-month-old grass carp. In addition, the two food odors stimulation differentially induced ORs expression in three tissues, especially strong in the olfactory bulb and brain. Altogether, our results suggest that olfaction plays an important role in the regulation of fish feeding behavior and provide a basis for further studies on the function and specific recognition of olfactory receptors in fish.

In-Situ Fish Heart-Rate Estimation and Feeding Event Detection Using an Implantable Biologger

Monitoring of physiology and behavior of marine animals living undisturbed in their natural habitats can provide valuable information about their well-being and response to environmental stressors. We focus on detecting the feeding behavior in predatory fish using implantable biologgers that record and analyze electrocardiogram (ECG) signals. We propose a novel processing pipeline for resource-constrained embedded systems that can infer higher-level information, such as heart-rate and feeding events, from the ECG signals in situ. Our main contributions are in proposing efficient event detection algorithms that can reliably detect fish feeding events from noisy heart-rate data based on the unique statistical properties of feeding-induced changes in the heart-rate. We evaluate our approaches using an in-house biologger that we surgically implant in twelve coral trout fish and use to collect data during an experiment for a period of ten weeks and show that our signal processing pipeline performs well with noisy ECG signals overall. Specifically, our heart-rate estimation algorithm achieves errors of less than one beat per minute even in scenarios where popular algorithms used by domain specialists perform poorly. Furthermore, our feeding detection algorithms offer improved accuracy compared with the state-of-the-art algorithms while requiring significantly reduced computational and energy resources. We implement the proposed heart-rate estimation and feeding detection algorithms on the biologger and evaluate the associated system overhead. The results show that our proposed heart-rate estimation and feeding detection algorithms can run in-situ on the biologger as they demand rather small computational and energy resources that can conveniently be provisioned. This work is an important first step towards developing effective tools for long-term monitoring of high-level parameters pertaining to the health and behavior of marine animals in the wild.

Research on feeding behavior of fish by using spatial and temporal features of depth images

Research on feeding behavior of fish by using spatial and temporal features of depth images

Digital Twin Architecture Evaluation for Intelligent Fish Farm Management Using Modified Analytic Hierarchy Process

Precision aquaculture deploys multi-mode sensors on a fish farm to collect fish and environmental data and form a big collection of datasets to pre-train data-driven prediction models to fully understand the aquaculture environment and fish farm conditions. These prediction models empower fish farmers for intelligent decisions, thereby providing objective information to monitor and control factors of automatic aquaculture machines and maximize farm production. This paper analyzes the requirements of a digital transformation infrastructure consisting of five-layered digital twins using extensive literature reviews. Thus, the results help realize our goal of providing efficient management and remote monitoring of aquaculture farms. The system embeds cloud-based digital twins using machine learning and computer vision, together with sensors and artificial intelligence-based Internet of Things (AIoT) technologies, to monitor fish feeding behavior, disease, and growth. However, few discussions in the literature concerning the functionality of a cost-effective digital twin architecture for aquaculture transformation are available. Therefore, this study uses the modified analytical hierarchical analysis to define the user requirements and the strategies for deploying digital twins to achieve the goal of intelligent fish farm management. Based on the requirement analysis, the constructed prototype of the cloud-based digital twin system effectively improves the efficiency of traditional fish farm management.

Fish school feeding behavior quantification using acoustic signal and improved Swin Transformer

In aquaculture, the real-time quantification of fish feeding behavior is an important basis for feeding decisions. The acoustics produced by fish chewing feed and activities during feeding can be used to quantify feeding behavior. This study proposes an Audio Spectrum Swin Transformer (ASST) model based on an acoustic signal and attention mechanism that can divide the feeding intensity of fish into four grades: strong, medium, weak, and none. The specific implementation methods are: (1) A sliding window was applied to clip the audio and the acoustic. Signals are transformed into spectrograms. (2) The perceptual domain of fish feeding acoustic spectrum recognition task was gradually expanded by adopting the Swin Transformer and utilizing its hierarchical structure. (3) The model's performance for small data sets was further improved by adding SPT, LSA, and enhanced residual connections. (4) A predictive optimization module was designed to correct the feeding strategy according to four feeding levels. The final experimental results demonstrate that the accuracy of the improved ASST network for fish feeding behavior quantification reached 96.16%. It can effectively identify-four fish feeding intensities, allowing on-demand feeding and providing a basis for developing intelligent feeding machines.

Feeding strategy and diet overlap in major fish stocks of Ayima and Gelegu Rivers, northwestern Ethiopia

AbstractRiverine ecosystems contribute considerable fisheries production. However, in Ethiopia, there is a lack of comprehensive biological studies on such ecosystems. This study assessed the feeding habits of commercially important fishes of the Ayima and Gelegu Rivers. Fishes' guts were sampled seasonally from April 2017 to November 2018 using gill nets, cast nets and electrofishing. Four hundred and forty‐nine stomachs were analysed. Niche breadth and overlap indices were estimated using Levin and Schoener indices, respectively. The data indicated that Clarias gariepinus (Burchell, 1822), Bagrus docmak (Forsskål, 1775) and Hydrocynus forskahlii (Cuvier, 1819) mostly preyed on fish, whereas Heterotis niloticus (Cuvier, 1829) and Labeobarbus bynni (Forsskål, 1775) consumed detritus and macrophytes. H. niloticus, L. bynni and H. forskahlii were generalist feeders. There was a slight ontogenetic dietary shift observed in some predator fishes. In smaller sized L. bynni (6–20 cm, TL), insects and ostracods were important preys and their contribution differed significantly (p &lt; 0.05). In H. forskahlii, the volumetric contribution of bivalves and detritus differed significantly amongst the different size classes. Generally, resource availability had little effect on the feeding behaviour of most fishes. This implies that fishes in floodplain rivers can forage any available prey without preference and accustom to a general feeding strategy to form biologically insignificant dietary overlap.

Dietary optimum inclusion level of jack mackerel meal for olive flounder (Paralichthys olivaceus, Temminck & Schlegel, 1846)

Feed stimulants are used to incite feeding behavior in fish to improve feed consumption and growth. This study investigated the effect of jack mackerel meal (JM) as dietary attractant and/or stimulants on olive flounder on growth, feed utilization, chemical composition, plasma chemistry, and non-specific immune responses. In addition, the attractiveness of the experimental diets containing various levels of JM to olive flounder was evaluated. Six hundred and seventy five juveniles were randomly assigned to 27 tanks each containing 25 fish [initial weight of 5.3 ± 0.01 g (SEM)] in the 8-week feeding trial. The control diet (JM0) contains 60% fish meal (FM). The graded levels (1, 3, 5, 20, 20, 40, 60, and 100%) of JM were included at the expenses of FM, referred to as the JM1, JM3, JM5, JM10, JM20, JM40, JM60, and JM100 diets, respectively. Fish were hand-fed to visual satiation in triplicate groups of each diet twice daily for 8 weeks. To determine the attractiveness of the experimental diets, thirty fish were confined in the acclimatization chamber at a time and a knockout comparison of the experimental diets was applied. The strongest feeding attractant response of olive flounder was observed in the JM60 diet. Attractiveness of fish to the JM40 diet was also higher than that of fish to the JM20 diets. Weight gain and SGR of fish fed the JM20 diet was significantly (P < 0.0001) greater than those of fish fed the JM0, JM1, JM3, JM5, JM10, and JM100 diets, but not significantly (P > 0.05) different from those of fish fed the JM40 and JM60 diets in the 8-week feeding trial. The highest feed consumption was obtained in fish fed the JM20 diet. None of the organosomatic indices (condition factor, and viscerosomatic and hepatosomatic indices), biochemical parameters, non-specific immune responses (superoxide dismutase and lysozyme activities), chemical composition and amino acid and fatty acid profiles of the fish whole-body were affected by the experimental diets. In conclusion, the strongest attractiveness of fish was observed in the JM60 diet, but the optimal inclusion level of JM in the diets seemed to be 20% for juvenile olive flounder.

Trophic functioning of integrated rice-fish farming in Madagascar: Insights from stable isotopes (δ13C & δ15N)

Trophic dynamics of integrated rice and fish farming (IRF) systems were studied in irrigated rice plots stocked with common carp in Madagascar. Fish feeding behavior was assessed by analyzing stable carbon and nitrogen isotopes of fish and natural feed sources. Stable isotope signatures of 45-day-old carp fry introduced into rice plots (δ13C = −18.8‰ ± 0.5‰, δ15N = 9.3‰ ± 0.6‰) revealed that they had been fed chicken egg yolk and corn meal, in accordance with local practices. However, after a 100-day growing period in experimental rice plots, 13C and 15N depletion was observed for 145-day-old carp, indicating a change in feeding sources. Under extensive conditions, common carp that fed on rice roots, sediments and suspended particulate organic matter (i.e. plant debris and detritus) had a larger trophic niche (3‰2) than 45-day-old carp (1.2‰2). Overall, common carp feeding behavior and the trophic food web in the IRF system suggest that increasing natural productivity through organic fertilization will enhance ecological intensification of both rice and fish production.

A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis.

Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.

Mini DNA barcodes reveal the details of the foraging ecology of the largehead hairtail, Trichiurus lepturus (Scombriformes: Trichiuridae), from São Paulo, Brazil

Abstract The largehead hairtail, Trichiurus lepturus, is an opportunistic, voracious, and piscivorous predator. Studies of fish feeding behavior based on the analysis of stomach contents are limited by the potential for the visual identification of the ingesta. However, molecular tools, in particular DNA barcoding, have been used successfully to identify stomach contents. When morphological analyses are not possible, molecular tools can precisely identify the components of the diet of a fish based on its stomach contents. This study used mini barcoding to identify food items ingested by T. lepturus off the northern coast of São Paulo State, Brazil. Forty-six sequences were obtained and were diagnosed as belonging to six different fish species: Pimelodus maculatus, Paralonchurus brasiliensis, Isopisthus parvipinnis, Opisthonema oglinum, Harengula clupeola, and Pellona harroweri or as belonging to the genera Lycengraulis and Sardinella. Trichiurus lepturus is an opportunistic predator that will exploit an available prey of an appropriate size. The results indicate that these fish migrate to warmer waters, such as those found in estuarine environments, at certain times of the year, where they exploit prey species that reproduce in this environment. One example was Pimelodus maculatus, which was the prey species most exploited based on the analysis of the material collected.

Fish Feed Intake, Feeding Behavior, and the Physiological Response of Apelin to Fasting and Refeeding.

Feed is one of the most important external signals in fish that stimulates its feeding behavior and growth. The intake of feed is the main factor determining efficiency and cost, maximizing production efficiency in a fish farming firm. The physiological mechanism regulating food intake lies between an intricate connection linking central and peripheral signals that are unified in the hypothalamus consequently responding to the release of appetite-regulating genes that eventually induce or hinder appetite, such as apelin; a recently discovered peptide produced by several tissues with diverse physiological actions mediated by its receptor, such as feed regulation. Extrinsic factors have a great influence on food intake and feeding behavior in fish. Under these factors, feeding in fish is decontrolled and the appetite indicators in the brain do not function appropriately thus, in controlling conditions which result in the fluctuations in the expression of these appetite-relating genes, which in turn decrease food consumption. Here, we examine the research advancements in fish feeding behavior regarding dietary selection and preference and identify some key external influences on feed intake and feeding behavior. Also, we present summaries of the results of research findings on apelin as an appetite-regulating hormone in fish. We also identified gaps in knowledge and directions for future research to fully ascertain the functional importance of apelin in fish.

Could Fish Feeding Behaviour and Size Explain Prevalence Differences of the Nematode Eustrongylides excisus among Species? The Case Study of Lake Garda

The nematode Eustrongylides excisus is a parasite of freshwater fish- and fish-eating birds, with known differences on prevalence values among fish species. Thus, the present study aims to explore the hypothesis that the feeding behavior and the size of fish belonging to different trophic levels could explain such differences. For that, 14 sampling sites were selected to perform a fish parasitological survey on Lake Garda (Italy) during spring-summer 2020. Amplification of nuclear ribosomal internal transcribed spacer (ITS) rDNA sequences of nematodes morphologically ascribable to the genus Eustrongylides allowed to identify them as E. excisus. From the five studied fish species (Perca fluviatilis, Lepomis gibbous, Coregonus lavaretus, Alosa fallax lacustris and Micropterus salmoides), only three presented the parasite E. excisus: P. fluviatilis, L. gibbous and M. salmoides, with significant differences in prevalence values among species (p = 0.002). Additionally, there were differences in prevalence values within the same fish species captured from different sampling sites. Findings showed that mainly piscivorous fish were positive for E. excisus and how the prevalence was highest in M. salmoides. As regard the fish size, a negative correlation between body size and E. excisus was found in P. fluviatilis due to the feeding habit of juvenile perch which feed mainly zooplankton and benthic invertebrates (i.e., oligochaetes, which are the first intermediate hosts of E. excisus). The study findings advance novel knowledge in the field of pathogens of zoonotic importance in the aquatic environment.