Identify Fish Species Research Articles

New Record of the Grey Cutthroat, Synaphobranchus affinis (Anguilliformes: Synaphobranchidae) from the East Mariana Basin, Western Pacific Ocean

Two deep-sea eels collected from the East Mariana Basin in the western Pacific Ocean are described in this study. Based on their morphological features, two eel specimens were assumed to belong to the Gray cutthroat eel family, Synaphobranchidae. Mitochondrial DNA (mtDNA) genes have been widely used as genetic markers to identify fish species. To accurately identify the species of the two eel specimens, we sequenced the mitochondrial cytochrome c oxidase subunit I (COI) and 16S ribosomal RNA (16S rRNA) genes from the two eel specimens. The sequences from the specimens were 100% identical. The molecular phylogenetic tree confirmed that the two eel specimens were closely related to Synaphobranchus affinis with a bootstrap value of 100%. This is the first study to report new records of S. affinis from the East Mariana Basin in the western Pacific Ocean.

Features of the vertebral column and caudal fin skeleton for identifying Engraulis australis in food of the Australasian gannet Morus serrator, Hauraki Gulf, New Zealand.

Identifying fish species from their bone remains is employed in identifying the prey of aquatic animals. However, diagnostic skeletal descriptions are scarce for fish species prey found in the food of piscivorous birds and other marine predators in New Zealand. The present article addresses this knowledge gap, providing a diagnostic description for the vertebral column and the skeleton of the caudal fin of the Australian anchovy Engraulis australis inhabiting coastal waters in and around the Hauraki Gulf, New Zealand. The vertebral column of E. australis is divided into four morphologically distinct regions more complicated than the classical division in abdominal and caudal parts only and the drawing of characteristic-looking vertebral profiles. Each of these four regions is associated with characteristic vertebral profiles. These morphological descriptive parameters express a morphotype that may be linked with the swimming mode of the Australian anchovy. The skeleton of the caudal fin of E. australis showed distinctive characteristics that will be useful as diagnostic criteria to identify specimens of the Australian anchovy and separate them from the skeletal elements of other fish species found in the food of gannets and other marine predators in future studies.

The preliminary DNA barcoding dataset of fishes from Chinese coastal waters

DNA barcoding is a molecular method for species identification based on a short standardized DNA sequence derived from the 5’ region of the cytochrome c oxidase subunit I (COI) gene, which is widely applied in marine fish identification. In this study, DNA barcodes were generated for 1,671 specimens representing 309 morphological marine fish species belonging to 223 genera and covering 101 families from Chinese coastal waters. The mean Kimura 2-parameter (K2P) genetic distances within species, genera, families, and orders were 0.20%, 20.94%, 24.45%, and 25.34%, respectively. As expected, the mean interspecific distance was higher than the mean intraspecific distance. Phylogenetic trees based on Bayesian inference (BI) and neighbor-joining (NJ) methods agreed with the taxonomic classifications. A total of 306 operational taxonomic units (OTUs) were reidentified via automatic barcode gap discovery (ABGD) analysis. An estimated nucleotide substitution matrices showed a transversion bias compared to transitional mutation. The amino acid variations of the barcoding region were also examined, and 13 amino acid sites exhibited high variability. Overall, our findings confirmed that DNA barcoding is a highly reliable mean of identifying fish species in Chinese coastal waters, and that the barcode library established here would facilitate the monitoring and management of the marine fishery resources in China.

Multi-species Fish Identification using Hybrid DeepCNN with Refined Squeeze and Excitation Architecture

Fish play a prominent role in the food web and fish farming has value for both human consumption and tourist attractions. Due to the increasing importance of marine biodiversity, recognition of fish species has become a prominent task in monitoring the mislabelling of seafood and extinct species. This problem can be solved using traditional manual annotation on the images. To reduce manpower, cost, and tremendous time, deep learning approaches are used which always require large datasets. Therefore, fish species identification is a challenging task using disproportionately small data sets. In this research, we develop a new method by refining the squeeze and excitation network for the automatic fish species classification model to identify 23 different types of fish species. To achieve this, a hybrid framework using deep learning is proposed on a large-scale dataset and implemented transfer learning for a small-scale dataset. Deep learning methods can be used to identify fish in underwater images. In this study, we have proposed a new method of hybrid Deep Convolutional Neural Network (CNN) along with a Support Vector Machine (SVM) for classification. Additionally, the Squeeze and Excitation (SE) block has been improved for improved feature extraction. The proposed method achieved an accuracy of 97.90%. Then post-training with the small-scale dataset (Croatian) achieved an accuracy of 94.99% with an 11% improvement compared to Bilinear CNN (B-CNN) (Qui et al., 2018) and can be used in any underwater applications to identify fish species and avoid mislabelling of seafood.

Deciphering Eurasian otter (Lutra lutra L.) and seal (Phoca vitulina L.; Halichoerus grypus F.) diet: Metabarcoding tailored for fresh and saltwater fish species.

Long-lived top predators shape biodiversity structure in their ecosystems and predator-prey interactions are critical in decoding how communities function. Studies on the foraging ecology of seals and Eurasian otters in Western Europe are outdated and most studies solely performed traditional hard part analysis. Molecular metabarcoding can be used as an innovative noninvasive diet analysis tool, which has proven efficient and complementary to hard part analysis, however, lacking application in the wider North Sea area. In this study, DNA from digesta, collected between 2014-2020, were used to identify fish species in the diet of 47 Eurasian otters, 54 harbour seals and 21 grey seals by applying a next-generation metabarcoding approach. A newly designed 16S rRNA primer, providing the best coverage of >130 local marine and freshwater fish species, was used to amplify prey DNA from seal scats and otter gut content sampled from the North Sea and regional freshwater bodies. Frequent fish species included tench, ninespine stickleback and white bream in otters; hooknose and common roach in grey seals and Pleuronectidae and sand gobies in harbour seals. Bipartite network analysis showed a strong overlap of harbour and grey seal diets. Otter diet intersected with both seal species in terms of freshwater species. This study provides new knowledge about dietary composition and community assemblage of fish prey in otters and seals in the North Sea and regional freshwaters, and a new molecular tool to elucidate predator-prey interactions and interspecies competition in complex and changing ecosystems under pressure from anthropogenic activities.

From catch to plate

From catch to plate

The newly built Ribb Reservoir fisheries, Tana Sub‐basin, Ethiopia: New fishery establishment, diversity, production, challenges and management

AbstractIn the future, agriculture alone may not produce the food needed to feed the increasing world population. Hence, it can be augmented by developing irrigation and water resources, mainly fisheries resources. In Ethiopia, especially in the Tana Sub‐basin, the expanded irrigation practices by building dams and weirs along the tributaries of Lake Tana did not consider fisheries nor the development and management. The aim of this research was, therefore, to promote fisheries and integrate them into the irrigation systems of the Ribb Reservoir in Ethiopia. During the sampling time (from September 2020 to August 2021), primary and secondary data collection and discussions with experts and fishers were conducted. Although the dam prevented the upstream spawning migration of the Labeobarbus species, the reservoir created a new fisheries development. Water quality parameters, such as temperature (23.82 ± 1.38°C), pH (6.36 ± 0.64) and dissolved oxygen (8.84 ± 3.54 mgl‐1), were within the acceptable range for fish. Of the 11 identified fish species, L. nedgia and L. surkis were the most and the least common species, respectively. Riverine and cut spawner species were the sources of the population. However, Oreochromis niloticus (Nile tilapia) was restocked in 2017 to increase the livelihoods of fishers. The Fulton condition factor, K values of L. nedgia (1.63 ± 0.25), L. intermedius (1.70 ± 0.27), C. gariepinus (0.61 ± 0.13) and O. niloticus (1.65 ± 0.29), and their ‘b’ values were <3. The newly established fishery in the reservoir is the source of income for fishers, and its annual production has been shown to increase from 148.5 to 1034.1 tons/year, indicating fish are adapting to the reservoir. However, the fishery has faced many challenges, such as illegal fishing, fisher engagement, and fishing gears. Limiting the number of fishers, restrictions on fishing gears and seasonality of fishing are some of the mitigations.

Character-based diagnostic keys, molecular identification and phylogenetic relationships of threadfin breams (family: Nemipteridae) based on mitochondrial genes from the Southern coromandel Coast, India

DNA barcoding, primarily focusing on cytochrome c oxidase subunit I (COI) gene has been appraised as an effective tool for species identification. In this study, we focused on the marine fishes of Family Nemipteridae, one of the commercially important group distributed within the Coromandel Coast. The Partial sequences of COI and 16S rRNA of mitochondrial genes were analyzed for species identification and phylogenetic relationship of Nemipterus species (Nemipterus japonicus, Nemipterus peronii, Nemipterus bipunctatus, Nemipterus bathybius). Character-based identification approaches that categorize specimens to species using classification rules that compactly identify species in terms of key diagnostic nucleotides in selected gene sequences. Using the BLOG 2.0 software, species-specific diagnostic nucleotides were identified for the selected group of species. A data set of 198 mtCOI sequences was obtained from published resources and used to screen character-based molecular diagnostic keys for species in silico. Partial sequences of both the genes provided sufficient phylogenetic information to distinguish the four Nemipterus species indicating the usefulness of mtDNA-based approach in species identification. This study proves the use of mtDNA genes sequence-based approach is a support tool along with traditional taxonomy for identifying fish species at a faster pace.

Using Environmental DNA to Detect Whales and Dolphins in the New York Bight

Determining how cetaceans and other threatened marine animals use coastal habitats is critical to the effective conservation of these species. Environmental DNA (eDNA) is an emerging tool that can potentially be used to detect cetaceans over broad spatial and temporal scales. In particular, eDNA may present a useful complementary method for monitoring their presence during visual surveys in nearshore areas, and for co-detecting prey. In conjunction with ongoing visual surveys, we tested the ability of eDNA metabarcoding to detect the presence and identity of cetaceans in the New York Bight (NYB), and to identify fish species (potential prey) present in the area. In almost all cases in which humpback whales and dolphins were visually observed, DNA from these species was also detected in water samples. To assess eDNA degradation over time, we took samples in the same location 15 and 30 min after a sighting in seven instances, and found that eDNA often, but not always, dropped to low levels after 30 min. Atlantic menhaden were detected in all samples and comprised the majority of fish sequences in most samples, in agreement with observations of large aggregations of this important prey species in the NYB. While additional data are needed to better understand how factors such as behavior and oceanographic conditions contribute to the longevity of eDNA signals, these results add to a growing body of work indicating that eDNA is a promising tool to complement visual and acoustic surveys of marine megafauna.

Molecular Identification of Species Diversity Using Pelagic Fish Eggs in Spring and Late Autumn-Winter in the Eastern Beibu Gulf, China

The Beibu Gulf is considered as one of China’s four major fishing grounds, although the substantial overexploitation of fisheries has led to the collapse of many fish stocks, and to changes to spawning grounds in recent decades. Classifying fish eggs is an important way to monitor the recruitment process and identify the spawning sites of fish. However, the lack of a basis for morphological identification and difficulties in correctly identifying fish eggs based on morphological characteristics has limited scientific studies. In the present study, we identified fish eggs using molecular detection of cytochrome c oxidase subunit I and cytochrome b fragments. Ichthyoplankton surveys were conducted in the spring and late autumn–winter of 2020 in the eastern Beibu Gulf. Among the DNA extracted from the 873 chosen fish eggs, we successfully obtained 541 high-quality cytochrome c oxidase subunit I sequences and 41 high-quality cytochrome b sequences. We successfully identified 212 fish eggs (36.4%) from 32 species; 291 eggs (50.0%) showed ambiguous species delimitation, and 79 eggs (13.6%) could not be identified. Among the identified species, we found 25 species in spring and 25 species in late autumn–winter, out of which 18 species occurred in both seasons. We also obtained high resolution photographs of fish eggs at the species level for further morphological analysis and identification. The present study confirms the efficacy of using molecular methods to identify fish species from eggs and provides valuable information for protecting the spawning ground of economically valuable fish and for managing fishery resources.

Relationship between fish length and otolith dimensions of Yellow striped goatfish, Upeneus vittatus (Forsskal, 1775) along the Indian coast

Otoliths are widely used to estimate the age and growth of fishes and to identify fish species. This information plays a vital role in fisheries management and population dynamics studies. The information about fish length and otolith dimensions relationship of Upeneus vittatus is lacking from the Indian water. In this study, a total of 237 numbers of individuals were collected from October 2018 to March 2019 and left otolith was studied since there were no significant differences between right and left otoliths. The relationships between total length and otolith dimensions were estimated by linear regression and found a strong positive correlation. The maximum correlation was found between fish length and otolith length for Mumbai populations (R² = 0.9342). In contrast, Kakinada populations had the maximum correlation between fish length and otolith width (R² = 0.8957), and Puri populations had the maximum correlation between fish length and otolith weight (R² = 0.9101). Present study found a strong positive correlation between fish length and otolith dimensions. This finding provides new insights into the field and can be used for the growth study, fish population assessment, and fisheries management.

Marine fish assemblages of Eastern Brazil: An update after the world's largest mining disaster and suggestions of functional groups for biomonitoring long-lasting effects

When the Fundão dam collapsed in Brazil, 50 million m3 of iron ore tailings were released into the Doce river, resulting in the world's largest mining disaster. The contaminated mud was transported 668 km downstream of the Doce river and reached the Atlantic Ocean 17 days after the collapse. Seven months later, there was evidence that the tailings had reached the largest and richest coral reef formation in the South Atlantic Ocean. This study provides the first description of species composition, abundance, and diversity patterns of fish assemblages in estuaries, coastal areas, and coral reefs affected by the rupture of the mining dam in the Doce river. A linear mixed model (GLMM) was used to evaluate the influence of salinity on fish abundance across estuarine and coastal ecosystems. In addition, based on functional traits related to habitat use and feeding habits, this study identified fish species suitable as bioindicators of the long-lasting effects of this major mining disaster. Bottom trawls were used to sample five estuaries and their respective coastal areas, and a visual census was employed to sample five reef areas, representing an impact gradient. A total of 269 species were recorded in all three habitats, but only seven were shared among them. The results showed lower similarity in assemblages among estuarine areas compared to the coastal and reef areas. Species composition among estuaries and reef ecosystems was more heterogeneous. In contrast, coastal habitats exhibited high homogeneity. Salinity had no statistically significant effect on fish abundance either in estuaries (p = 0.22) and along the coast (p = 0.14). Twelve fish species were identified as suitable bioindicators for evaluating the long-lasting effects of resuspension of contaminated sediments. These species are commonly found in the ecosystems under the influence of the disaster inhabiting potentially contaminated substrates and substrate-associated benthic preys.

Marine diatom remains as bioindicators of the uses of pre-Hispanic fishing gear recovered in ritual contexts at Huanchaco, north coast of Peru

The aim of this study is to test whether marine diatom remains recovered from archeological fishing implements (fishing nets and metal fishhooks) found at pre-Hispanic sites of the Huanchaco coastline, north coast of Peru, can be used as indicators to infer whether they were used, and if yes, how they were used by ancient fishermen before their burial ritual. The combined analysis and quantification of fragmented and complete diatom valves from the fishing implements and control samples, which can help to identify potential contaminations from soil deposits, can indicate if the fishermen used them in their daily subsistence activities. This study can help to infer which marine zones were exploited by these ancient fishermen. Marine diatom analysis results were combined with identified fish species consumed and later discarded in the site’s middens. An interesting pattern emerges from the preliminary results of combining both datasets: while all fishing nets were intensively used, metal fishhooks seem to have been very occasionally sunk in the sea. Although these results could simply be the outcome of the type of materials tested and conservation issues, it prompted us to evaluate how fishing technology was used during the first part of the Early Intermediate Period (100–400/450 cal. A.D.) along the north coast of Peru and its impact on the daily subsistence activities of ancient pre-Hispanic fishing settlements.

Fish allergy tolerance 16 months after diagnosis.

Fish allergy is generally thought to be persistent, and approximately 80% of patients with fish allergies do not develop tolerance even 10 years after diagnosis. There have been no reports of rapid tolerance development in patients with severe fish allergies. We report the development of tolerance 16 months after the diagnosis of fish allergies. A 13-month-old boy was diagnosed with rosefish allergy (Sebastes matsubarae) and Japanese jack mackerel allergy (Trachurus japonicus). To find out which species of fish he could consume safely, he underwent several oral food challenge (OFC) tests. It was determined that he could consume tuna, salmon, cod, sardine, chub mackerel (Scomber japonicus), and Japanese amberjack (Seriola quinqueradiata) without eliciting signs of allergy. He continued to eat the fish that did not produce allergic reactions three to four times a week. The titer of serum allergen-specific immunoglobulin E (IgE) to fish had decreased in a subsequent ImmunoCAP®-specific IgE blood test performed 16 months after the diagnosis of the rosefish allergy. Following this test result, he underwent OFCs with rosefish and Japanese jack mackerel, both of which turned out to be negative, and it was determined that he had developed tolerance to fish. In this case, the repeated OFCs were useful in identifying fish species that were safe for consumption. In addition, the decrease in allergen-specific IgE was useful in predicting the development of tolerance. We hypothesize that proactive consumption of available fish species may lead to this rapid induction of tolerance to fish allergens.

Fish Species Detection Application (FiSDA) in Leyte Gulf Using Convolutional Neural Network

This study presents an application that employs a machine-learning algorithm to identify fish species found in Leyte Gulf. It aims to help students and marine scientists with their identification and data collection. The application supports 467 fish species in which 6,918 fish images are used for training, validating, and testing the generated model. The model is trained for a total of 4,000 epochs. Using convolutional neural network (CNN) algorithm, the best model during training is observed at epoch 3,661 with an accuracy rate of 96.49% and a loss value of 0.1359. It obtains 82.81% with a loss value of 1.868 during validation and 80.58% precision during testing. The result shows that the model performs well in predicting Malatindok and Sapsap species, after obtaining the highest precision of 100%. However, Hangit is sometimes misclassified by the model after attaining 55% accuracy rate from the testing results because of its feature similarity to other species.

Demersal Fish Diversity Index in Cirebon Waters, West Java

This study aims to analyze the distance between the placement of the base fish aggregating devices (FAD) from the beach, the composition of the catch, identify fish species and determine the diversity of fish caught at different distances of the base FAD in Cirebon waters. The research method was carried out descriptively by census. Data analysis method for demersal fish diversity index value (H') using Shannon-Wiener and t-test. Sampling of respondents was carried out by census as many as 20 basic FAD fishermen. The research location is in the Cangkol Tengah Fishermen Association, Cirebon City. The results of this study indicate that the number of fish species caught on FADs with a distance of <12 miles was obtained 10 species with 6,784 fish, the largest number of fish caught was snapper (Lutjanus Campechanus) as many as 2,040 fish with a percentage of 30.2% of the total number of fish. The whole fish caught, while the fish with the largest weight was snapper for distances <12 miles with an average weight of 658.92 kg or 30% of other types of fish. The number of fish species caught on FADs with a distance of >12 miles obtained 10 species with 9.217 fish, the highest number of fish caught was snapper (Lutjanus Campechanus) as many as 2,884 fish with a percentage of 27.97% of the total number of fish caught, while the fish with the greatest weight is snapper with an average weight for distances >12 miles of 988.39 kg or 30% of other types of fish. The magnitude of the demersal fish diversity index value at the base FAD placement distance <12 miles was 1.6672 and more than 12 miles was 1.8137 which means that there is no significant difference and the diversity index category is small because the value of H '≤ 2.

Otolith shape analysis of Lethrinus lentjan (Lacepède, 1802) and L. microdon (Valenciennes, 1830) from the Red Sea

Otolith shape and morphology are used to identify fish species and population stocks. The aim of this study was to distinguish the Lethrinus lentjan (Lacepede, 1802) and L. microdon (Valenciennes, 1830) (family: Lethrinidae) using otolith shape. The analyses apply the ShapeR package in R which enables to extract the outline and otolith morphology from images and for statistical examining of individual variation. Otoliths of 165 individuals from the two Lethrinus species were collected during 2019 and 2020. The wavelet levels were examined by using 6 wavelets to collect 63 coefficients. The regression between width and fish length were b = -0.03 (t = 2.6, P = 0.01) for L. lentjan and was significantly different (t = 2.120, P = 0.036) for L. microdon (b = 0.018).

Formalin-Fixed Fish Larvae Could Be Effectively Identified by DNA Barcodes: A Case Study on Thousands of Specimens in South China Sea

Delimiting ichthyoplankton is fundamental work for monitoring the recruitment process and identifying the spawning and nursing grounds of fishes. Nevertheless, it is extremely difficult to identify the fish during the early stages at the species level based on morphological characters because of the paucity of diagnostic features. In this study, we investigated the fish larval community through large-scale ecosystemic sampling in South China Sea (SCS) during 2013 and 2017 using DNA barcodes. To maintain the morphologies of fish larvae, we preserved the larvae in formalin and developed a technique to recover their DNA. Among the 3,500 chosen larvae, we successfully extracted DNA from 2,787 larval samples and obtained 1,006 high-quality sequences. Blast searches showed that 408 larvae (i.e., 40.5%) could be unambiguously identified to species, 413 larvae (i.e., 41.1%) were ambiguously species delimitation, and 185 larvae (i.e., 18.4%) showed a low match similarity with target sequences. A total of 101 species were identified, among which 38 and 33 species corresponded to demersal and reef-associated species, whereas the remaining 30 species corresponded to benthopelagic, pelagic-oceanic, bathypelagic, and pelagic-neritic species. High-quality larval photographs of the 101 diagnosed species showed intact morphological characters and thus provided a reference for identifying fish species during the early stages based on morphological characters. Our study highlighted the possibility of recovering and amplifying DNA from formalin-fixed samples and provided new insight into the fish larval community in the SCS.

Identification of Five Types Kampar River Fish With Genetic Approach

Fish identification in the mainland waters of Riau Province is generally carried out based on morphological characteristics. To identify fish species accurately in a short time, the DNA Barcoding method with a molecular approach is used. Five types of fish were taken from Kampar River and coded as MJ313, PJ36, SJ35, BJ33, and GJ41. The genes of these five fish were amplified, and PCR products were sequenced and analyzed using bioinformatics software. The editing of the sequencing results and the determination of the nucleotide composition were analyzed using Mega5 software. DNA sequences were aligned using the ClustalW software. The sequences obtained are then compared with GeneBank data using Basic Local Alignment Search Tools (BLAST). The results showed that the species with the code MJ313 and GJ41 were Channa striata, PJ36 were Osteochilus hasseltii, BJ33 were Mystus sp. Based on the sample examination, the species coded SJ35 cannot be determined because the marker band is low so that it is not visible. The results of the analysis on species coded SJ35 indicate that there is no COI report for species coded SJ35 on GeneBank.

Fish Species Recognition with Faster R-CNN Inception-v2 using QUT FISH Dataset

Fish species conservation had a big impact on the natural ecosystems balanced. The existence of efficient technology in identifying fish species could help fish conservation. The most recent research related to was a classification of fish species using the Deep Learning method. Most of the deep learning methods used were Convolutional Layer or Convolutional Neural Network (CNN). This research experimented with using object detection method based on deep learning like Faster R-CNN, which possible to recognize the species of fish inside of the image without more image preprocessing. This research aimed to know the performance of the Faster R-CNN method against other object detection methods like SSD in fish species detection. The fish dataset used in the research reference was QUT FISH Dataset. The accuracy of the Faster R-CNN reached 80.4%, far above the accuracy of the Single Shot Detector (SSD) Model with an accuracy of 49.2%.