Fish Counts Research Articles

Effects of installation depth and recording start time on fish count and mean fork length of <i>Seriola quinqueradiata</i> measured by optical stereo cameras

Effects of installation depth and recording start time on fish count and mean fork length of <i>Seriola quinqueradiata</i> measured by optical stereo cameras

A video monitoring and computational system for estimating migratory juvenile fish abundance in river systems

AbstractDiadromous fishes migrate between marine and fresh waters for reproduction. For anadromous species, which spawn in freshwater, improved access to freshwater spawning and nursery habitats and ability of juveniles to emigrate to the ocean may support population recovery. Despite the potentially enormous influence of early life stage survival on adult population size, managers and scientists have limited capacity to assess numbers of juvenile anadromous fishes leaving freshwater ecosystems. Such data are critical for evaluating reproductive success and habitat suitability and have been identified as a top priority in anadromous fish research and management. We developed a state‐of‐the‐art underwater video and computational system to collect videos to estimate abundances and migration timing for juvenile river herring (Alosa pseudoharengus; Alosa aestivalis). We collected continuous video in the Monument River (Bourne, Massachusetts, USA) from June to November 2017. We trained three types of neural network models to detect and count fish in video frames and evaluated model performance by comparing human counts to model outputs. Our top model assessed presence and absence (F1 = 87%) and counted fish (counting error 9.4%) with an accuracy comparable to human counters (F1 = 88%). Our system's capability to collect accurate counts of emigrating juveniles will provide critical information that could be related to the numbers of spawning adults, system‐specific productivity, and spawning and nursery habitat suitability. Both the video collection system and computational model may be transferrable to other sites and for other species where tracking juvenile emigration may inform management efforts.

Long-term monitoring of the fish community in the Minho Estuary (NW Iberian Peninsula).

The paper presents an extensive fish sampling dataset spanning a long-term period from 2010 to 2019. The data were collected in Lenta Marina, an upstream area in the Minho Estuary of the NW Iberian Peninsula, which belongs to a LTSER (Long-Term Socio-Ecological Research) platform. To capture fish, fyke nets were utilised as the sampling method and deployed at Lenta Marina. This dataset offers valuable insights into the abundance of each collected taxa recorded over time. The dataset reports a comprehensive compilation of data on the abundance of fish species observed in the area during the sampling period (includes zeroes when a given taxonomic entity was absent in a given sampling event). It provides a detailed record of the abundances of the fish community through time in a frequent sampling regime (on average, sampling was done every 6 days). The dataset shows that the amount of fish from invasive taxa exceeds the count of fish from native taxa in the Minho Estuary.

Transferable Deep Learning Model for the Identification of Fish Species for Various Fishing Grounds

The digitization of catch information for the promotion of sustainable fisheries is gaining momentum globally. However, the manual measurement of fundamental catch information, such as species identification, length measurement, and fish count, is highly inconvenient, thus intensifying the call for its automation. Recently, image recognition systems based on convolutional neural networks (CNNs) have been extensively studied across diverse fields. Nevertheless, the deployment of CNNs for identifying fish species is difficult owing to the intricate nature of managing a plethora of fish species, which fluctuate based on season and locale, in addition to the scarcity of public datasets encompassing large catches. To overcome this issue, we designed a transferable pre-trained CNN model specifically for identifying fish species, which can be easily reused in various fishing grounds. Utilizing an extensive fish species photographic database from a Japanese museum, we developed a transferable fish identification (TFI) model employing strategies such as multiple pre-training, learning rate scheduling, multi-task learning, and metric learning. We further introduced two application methods, namely transfer learning and output layer masking, for the TFI model, validating its efficacy through rigorous experiments.

Life‐history connections to long‐term fish population trends in a species‐rich temperate river

AbstractFishes exhibit a diverse range of traits encompassing life‐history strategies, feeding behaviours and spawning behaviours. These traits mediate fish population responses to changing environmental conditions such as those caused by anthropogenic stressors. The Conasauga River, located in northwestern Georgia and southeastern Tennessee, USA, hosts a diverse assemblage of over 75 species of freshwater fish, some of which are locally or regionally endemic, and many of which are imperilled. Annual monitoring data have shown population declines in multiple fish species of conservation concern in the Conasauga River since at least the 1990s, raising the possibility that other taxa could be declining as well. We quantified temporal changes in fish communities at six shoal sites sampled annually in most years from 1996 to 2022, and asked whether species traits hypothesized to underlie population vulnerability to environmental alteration were correlated with species‐specific trends for 32 taxa. We estimated that total counts of fish in annual samples declined by ~2% per year, although declines were uneven among species and generally greater for less abundant taxa. Tests for species traits corresponding to temporal population trends provided evidence that crevice‐spawning minnows and smaller‐bodied taxa had steeper declines compared with broadcast spawners and larger, longer‐lived, more fecund taxa. Lower abundance, reliance on a particular habitat feature, and life‐history traits that may limit population resilience to disturbance may all prove useful for identifying riverine fishes at particular risk of future population decline.

Monitoring of Fish Migration in Fishways and Rivers—The Infrared Fish Counter “Riverwatcher” as a Suitable Tool for Long-Term Monitoring

With the globally increasing awareness regarding the interconnectivity between freshwater ecosystems, projects for re-establishing connectivity with fishways as well as stock management are increasing. To ensure the quality and impact of such projects and for extending the scientific state of knowledge on this topic, a detailed monitoring of these measures is mandatory. Automatic, passive, and contactless counting devices can play a major role in these long-term monitoring projects, both for animal protection (non-invasive methods) and technical issues (comparatively low-cost systems in terms of time). Infrared fish counters can be used in a variety of fisheries applications and have proven particularly valuable in situations when long-term or continuous monitoring in lotic systems is required. Herein, we describe the function and technical capabilities of the VAKI Riverwatcher fish counter, provide information regarding its installation, and highlight some specific, practical applications of this technology. We discuss how the Riverwatcher can be used to validate the functionality of fish passage structures and to provide abundance estimates of migratory fish populations. Finally, we call attention to the challenges associated with operating monitoring equipment in variable river systems and briefly discuss potential sources of error that can influence the monitoring results and approaches to minimize or alleviate these errors.

Underwater fish detection and counting using image segmentation

Underwater fish detection and counting using image segmentation

Diadromous fish run assessment: a double-observer model using acoustic cameras to correct imperfect detection and improve population abundance estimates

IntroductionDiadromous fish populations have strongly declined over decades, and many species are protected through national and international regulations. They account for less than 1% of fish biodiversity worldwide, but they are among the most perceptible linkages between freshwater and marine ecosystems. During their migration back and forth, diadromous fish species are subjected to many anthropogenic threats, among which river damming can severely limit access to vital freshwater habitats and jeopardize population sustainability. Here, we developed a method based on a double-observer modeling approach for estimating the abundance of diadromous fish during their migration in rivers.MethodsThe method relies on two independent and synchronous records of fish counts that were analyzed jointly thanks to a hierarchical Bayesian model to estimate detection efficiencies and daily fish passage. We used simulated data to test model robustness and identify conditions under which the developed approach can be used. The approach was then applied to empirical data to estimate the annual silver eel run in the Touques River, France.ResultsThe analysis of simulated datasets and the study case gives evidence that the model can provide robust,accurate, and precise estimates of detection probabilities and total fish abundance in a set of conditions dependent on the information provided in the data (annual distribution of fish passage, annual number of observation, pairing period, etc.).DiscussionThen, the method can be applied to various species and counting systems, including nomad acoustic camera devices. We discuss its relevance for programs on river continuity restoration, notably to quantify population restoration associated with dam removals.

Impact of Dead Fish Degradation on the Monitoring of Fish Losses in the Pregrowing and Growing Phase of Caged European Sea Bass (Dicentrarchus labrax)

The interplay between environmental, biological, and physical factors often leads to the deterioration of dead fish in marine cages prior to their removal. Depending on the weight of the dead fish and the frequency of their removal, deterioration can progress to a stage where visual identification by divers becomes challenging, thereby disrupting accurate counting of dead fish. This study presents a practical precision tool for monitoring the number of dead fish during the pregrowth and growth phases of caged European sea bass (Dicentrarchus labrax). To improve the assessment of collected mortality, experiments were conducted in farming cages with various fish weights. Identifiable fish rates (I, %) were calculated every 24 hr and classified into four weight classes: WC1 (4–15 g), WC2 (15–30 g), WC3 (30–80 g), and WC4 (>80 g). The corrected number of dead fish (M) was calculated by dividing the collected number (C) by a correction factor (McR), which was determined based on the adopted removal frequency. The possible mortality removal frequencies per week (Fn) included operations such as F7 (daily), F3 (3 times), F2 (2 times), and F1 (once). The smallest correction denominator was 22% for WC1 at a frequency of once per week, whereas the maximum was 100% for WC3 and WC4 daily. The results revealed a high negative significant correlation between Fn and uncollected degraded fish rate (UR) (r = –0.841, p < 0.05). Applying corrections to mortality collected in three finished batches (B2, B3, and B7) led to an increase in the mortality rate by 3.9% ± 1.5%, 5.5% ± 0.7%, and 5.0% ± 0.5%, respectively. This explained 16.8% ± 4.7%, 65.5% ± 26.7%, and 30.3% ± 3.7% of fish disappearances in B2, B3, and B7, respectively. The significance of this study lies in its practical applicability to fish farms as a precise tool for monitoring fish raised in marine cages.

Enumeration potential of environmental DNA for Pacific salmon stock assessments

AbstractThe field of environmental DNA (eDNA) has advanced over the past decade, with multiple approaches available for a variety of sampling media and species. While using eDNA for the purpose of simply detecting species is becoming a routine process, the utility of eDNA to estimate species abundance is not well understood. Here, we quantify salmon environmental DNA upstream of a fish counting fence along with river velocity, and together, use these values to determine the correlation between the number of salmon passing by the fish fence daily with daily eDNA rates in water before, during, and after the salmon spawning season for four Pacific salmonids (Oncorhynchus gorbuscha, O. kisutch, O. tshawytscha, and O. nerka; pink, coho, chinook, and sockeye, respectively). Throughout the spawning season, approximately 182,000 salmon were counted passing through the fence, of which >98% were pink salmon. Pink salmon exhibited strong correlation between human counts (effect size = 0.65, SE = 0.040) to eDNA rates in the present study and exhibited day‐to‐day variation and a unimodal profile rising and falling with human fish counts. However, the salmon species observed in much lower numbers exhibited a much weaker correlation with eDNA levels higher during the pre‐migratory period than during the migratory period for sockeye, coho, and chinook. Thus, for salmon spawning runs with less than ~1000 adults and daily counts of less than ~100, the juvenile and/or prior seasons eDNA signal appears to be indistinguishable from the adult spawning eDNA signal in our river system. However, for the large pink salmon run, eDNA rates appeared to reflect a local signal of salmon in space and time, essentially tracking these fish within days of passing through the eDNA sampling site.

Fish counting through underwater fish detection using deep learning techniques

Fish counting through underwater fish detection using deep learning techniques

Diversity and Current State of Fish Communities of the Reserve “Utrish” (Caucasian Coast of the Black Sea, Abrau Peninsula)

Abstract The article is focused on the current state of biodiversity in the aquatic environment of the Utrish Nature Reserve. Implementation of the multi-level monitoring program for fish and crustacean fauna, conducted in the waters of the Utrish Nature Reserve during the period 2016-2018, using classical methods of biological analysis, as well as visual underwater fish counting, allowed us to assess the overall state of biodiversity in this area as sufficiently stable. The population status of the model indicator species, the European black scorpionfish Scorpaena porcus, was also stable during this period, confirming the effectiveness of protective measures in this area. Due to the continued increase in anthropogenic pressure on coastal marine biocenoses, monitoring studies require further continuation.

Identification of antimicrobial peptide genes from transcriptomes in Mandarin fish (Siniperca chuatsi) and their response to infection with Aeromonas hydrophila

Mandarin fish (Siniperca chuatsi) is a valuable freshwater fish species widely cultured in China. Its aquaculture production is challenged by bacterial septicaemia, which is one of the most common bacterial diseases. Antimicrobial peptides (AMPs) play a critical role in the innate immune system of fish, exhibiting defensive and inhibitory effects against a wide range of pathogens. This study aimed to identify the antimicrobial peptide genes in mandarin fish using transcriptomes data obtained from 17 tissue in our laboratory. Through nucleotide sequence alignment and protein structural domain analysis, 15 antimicrobial peptide genes (moronecidin, pleurocidin, lysozyme g, thymosin β12, hepcidin, leap 2, β-defensin, galectin 8, galectin 9, apoB, apoD, apoE, apoF, apoM, and nk-lysin) were identified, of which 9 antimicrobial peptide genes were identified for the first time. In addition, 15 AMPs were subjected to sequence characterization and protein structure analysis. After injection with Aeromonas hydrophila, the number of red blood cells, hemoglobin concentration, and platelet counts in mandarin fish showed a decreasing trend, indicating partial hemolysis. The expression change patterns of 15 AMP genes in the intestine after A. hydrophila infection were examined by using qRT-PCR. The results revealed, marked up-regulation (approximately 116.04) of the hepcidin gene, down-regulation of the piscidin family genes expression. Moreover, most AMP genes were responded in the early stages after A. hydrophila challenge. This study provides fundamental information for investigating the role of the different antimicrobial peptide genes in mandarin fish in defense against A. hydrophila infection.

Understanding and optimizing fish counting techniques based on electrical impedance measurements.

Sustainable aquatic resources management requires reliable methods for fish detection in various environmental conditions. Herein, we study fundamental mechanisms underlying the application of electrical impedance measurements in this regard. We present results of experimental studies conducted in laboratory conditions using a low-cost impedance measurement circuit, as well as the corresponding numerical models. We also present evaluation results of a newly developed, real-time detection algorithm based on adaptive thresholding. The numerical model was validated by extracting fish tracks in 3D space from the experimental datasets, and then comparing the calculated versus measured impedance values as functions of fish coordinates in time. Numerical predictions closely resemble the experimental data. The detection sensitivity and specificity values determined for various settings exceeded 90%. Electrode width to spacing ratio is demonstrated to be a crucial parameter influencing the system sensitivity distribution. The introduced approach can constitute a framework for designing electrical impedance-based fish counting systems.

A class of their own? Water-soluble polymer pollution impacting a freshwater host-pathogen system

While the inclusion of synthetic polymers such as primary microplastics within personal care products have been widely restricted under EU/UK Law, water-soluble polymers (WSPs) have so far slipped the net of global chemical regulation despite evidence that these could be polluting wastewater effluents at concentrations greatly exceeding those of microplastics. Polyvinyl alcohol (PVA) and polyvinylpyrrolidone (PVP) represent WSPs with common industry and household uses, down-the-drain disposal and a direct route to wastewater treatment plants, conveying high risk of environmental leaching into freshwater ecosystems. The current study is the first investigating the impacts of predicted environmental concentrations of these WSPs on life-history traits of two freshwater species also constituting a disease model (fish - Poecilia reticulata and parasite - Gyrodactylus turnbulli). Single effects of WSPs on fish as well as their interactive effects with infection of the ectoparasite were determined over a 45-day exposure. Generally, WSPs reduced fish growth and increased routine metabolic rate of fish implying a depleted energetic budget, however these effects were dose, exposure time and polymer dependent. Parasitic infection alone caused a significant reduction in fish growth and enhanced fish routine metabolic rate. In contrast, a non-additive effect on metabolic rate was evident in fish experiencing simultaneous infection and WSP exposure, suggesting a protective effect of the two WSPs for fish also exposed to a metazoan ectoparasite. Off-host parasite survival was significantly lowered by both WSPs; however, parasite counts of infected fish also exposed to WSP were not significantly different from the control, implying more complex mechanisms may underpin this stressor interaction. Distinct detrimental impacts were inflicted on both organisms implying environmental leaching of WSPs may be causing significant disruption to interspecies interactions within freshwater ecosystems. Additionally, these results could contribute to sustainable development in industry, as we conclude PVA represents a less harmful alternative to PVP.

Changes in species compositions of fish in the bays of the Northwestern Gulf of Mexico

IntroductionLocal biodiversity is increasing in many temperate and subtropical waters due to climate change. It is often caused by shifting fish distributions, thus the biodiversity gradient, from lower to higher latitudes. However, these shifts in distributions do not occur uniformly across all species. Consequently, communities are not only shifting their spatial distributions, but species compositions are also changing. We investigated spatiotemporal differences in the compositions of fish species in the bays of the northeastern Gulf of Mexico and identified species that contribute to the temporal changes.MethodsWe used fish count data collected using gillnets in eight major bays, encompassing over 600 km of coastline, during spring and fall seasons from 1982 to 2019. The nonmetric multidimensional scaling (NMDS) on the Bray-Curtis dissimilarity index among species composition vectors was used to detect the differences in species composition, and the similarity percentages (SIMPER) were used to determine the contribution of species to the differences. ResultsThe result shows there was a gradual change in species composition in all bays over the years, and the composition was different among bays and seasons. The species contributing to the temporal changes included those that are expanding (e.g., Common snook, Centropomus undecimalis, and Smallscale fat snook, C. parallelus) as well as retracting (e.g., Southern flounder, Paralichthys lethostigma, and Spanish mackerel, Scomberomorus maculatus) their distributions toward the north. The species observed only in recent years in these bays tended to have a preference for warmer water (e.g., Gulf pipefish, Syngnathus scovelli, and Chain pipefish, S. louisiana). DiscussionThe results are consistent with the potential effects of climate change. However, the salinity of the bays in the study area generally exhibits an increasing trend from the northern to southern bays. The spatial salinity gradient has a substantial impact on species compositions, indicating that species distributions are influenced by multiple environmental conditions. This complexity makes our ability to accurately predict future species compositions under changing environmental conditions challenging.

Boosting fish counting in sonar images with global attention and point supervision

Automatically counting fish in sonar images has been attracting increasing attention in recent years because extreme efforts are needed in manual counting. Density map regression provides a promising approach in the counting field, but two obstacles are placed in front of fish counting in low resolution sonar images: the difficulty in distinguishing fish from the similar background noise and the inconsistency between the strip-shaped fishes in input images and dot-shaped ground truth density map. To address these issues, we present GPNet, a novel encoder-decoder network with global attention and point supervision, to boost sonar image-based fish counting accuracy. To alleviate the impact of background noise, we incorporate a segmentation module (SM) with global self-attention to the neck of the network to identify the fish region and space out background noise. Furthermore, feature enhancement modules (FEM) with a global receptive field are introduced to the encoder to enhance the feature representation and discrimination. To break down the performance upper bound resulting from target shape inconsistency between input and ground truth, we leverage fish center coordinates instead of the Gaussian density map to supervise the network training directly. Extensive experiments on a challenging public sonar image-based fish counting dataset, the ARIS dataset, demonstrate that GPNet achieves state-of-the-art performance both in counting accuracy and noise removal.

Hypoxia-induced HIF-1α promotes Listeria monocytogenes invasion into tilapia.

HIF-1α is a nuclear transcription factor, and its activity is tightly regulated by the level of available oxygen in cells. Here, we investigated the roles of HIF-1α in the invasion of Listeria monocytogenes into tilapia under hypoxic environments. We found that the expression levels of HIF-1α in examined tissues of hypoxic tilapia were significantly upregulated, indicating that the tissue cells have been in hypoxic conditions. After 24-h infection with L. monocytogenes, we found that bacterial burden counts increased significantly in all examined tissues of hypoxic fish. To explore why the bacterial count increased significantly in the tissues of hypoxic fish, we modulated HIF-1α expression through RNAi technology. The results indicated that c-Met expression levels were positively related to HIF-1α expression. Since c-Met is the receptor of InlB that plays critical roles in the adhesion and invasion of L. monocytogenes, the ∆InlB strain was used to further explore the reason for the significant increase in bacterial counts in hypoxic fish. As expected, the decrease in the adhesion ability of ∆InlB suggested that InlB mediates L. monocytogenes infection in tilapia. After being infected with ∆InlB strain, we found that the bacterial counts in hypoxic fish were not affected by hypoxic conditions or HIF-1α expression levels. These findings indicate that HIF-1α may promote the internalization of InlB by upregulating c-Met expression and therefore contributes to the invasion of L. monocytogenes into hypoxic tilapia. IMPORTANCE Listeria monocytogenes is a zoonotic food-borne bacterial pathogen with a solid pathogenicity for humans. After ingestion of highly contaminated food, L. monocytogenes is able to cross the intestine invading phagocytic and nonphagocytic cells and causes listeriosis. China is the world's largest supplier of tilapia. The contamination rate of L. monocytogenes to tilapia products was as high as 2.81%, causing a severe threat to public health. This study revealed the underlying regulatory mechanisms of HIF-1α in the invasion of L. monocytogenes into tilapia under hypoxic environments. This study will be helpful for better understanding the molecular mechanisms of hypoxic environments in L. monocytogenes infection to tilapia. More importantly, our data will provide novel insights into the prevention and control of this pathogen in aquaculture.

Seasonal Variation of Quality and the Total Viable Count of Lean and Fatty Fish

Seasonal Variation of Quality and the Total Viable Count of Lean and Fatty Fish

Leveraging the feature distribution calibration and data augmentation for few-shot classification in fish counting

Fish counting is critical to the success of fish farming since it serves as the foundation for evaluating fish health and growth, rationalizing feed and water quality management, and so on. To address the issue of unbalance distribution and a limited number of samples in the actual fish counting process, a distribution calibration method based on linear fitting was proposed, achieving accurate classification of few-shot or even no-shot classes without complicated generative models and parameter settings. Firstly, seven features were extracted from the adherent fish, followed by a linear fit of the features to the number of adherent fish; subsequently, the number of adherent fish is considered a class, and the mean and variance of the few-shot classes are calibrated based on the fitted linear relationship; following that, adequate training data for the classifiers are generated by extracting samples from the calibration distribution, and the machine learning classifiers model are trained to accurately classify the number of adherent fish. By testing and analyzing the datasets with 4 sets of long-tailed distributions and nine machine learning classifiers, the P, R, F1 scores of Support Vector Classification(SVC) classifier with Radial Basis Function(RBF) achieved 85.6% (±0.4%), 85.5% (±0.6%), and 85.5% (±0.5%), respectively, while R2 was 0.924 (±0.003), with an average counting accuracy of over 96.2% for each class. In comparison to other classifiers, the SVC classifier demonstrated superior precision and stability.