Fatty acid profile variations after exposure to textile industry effluents in Indian Major Carps

Indian major carps are the widely consumed fish species of Pakistan, being a cheap source of proteins and unsaturated fatty acids, they are good for cardiovascular health. Water pollution due to discharge of untreated industrial waste water into water bodies contaminates this precious source of nutrients. The present study therefore, was aimed to assess deterioration of fatty acid profile of three Indian major carp species due to different concentrations of industrial wastes. The water samples were collected from the river Chenab at the site where it receives industrial wastewater via Chakbandi drain. After exposure to 1.5%, 3.0%, and 4.5% dilutions of collected water in different aquaria it was observed that proportion of unsaturated fatty acids in selected fish species were decreased significantly as the intensity of the dose increased (P < 0.05). Conversely the level of saturated fatty acids increased with the increasing dose of treatment (P < 0.05). These findings suggest that untreated wastewater not only deteriorate the fatty acid profile of aquatic animals but also these toxic substances can reach human body through fish meat and pose further health hazards. Therefore, it is highly recommended that industrial effluents should be treated before they are dumped into water bodies.

Length-weight relationship: eight species of Cyprinidae from river Panjkora, Khyber Pakhtunkhwa, Pakistan

Seven hundred and twenty four fish specimens were captured from March to September 2016. The materials used in the current study were cast nets, hand nets. Eight cyprinid fish species were studied for their length-weight relationships. Parameter b in the LWR was 3.03, 3.06, 3.02, 2.29, 2.82, 3.43, 2.73 and 2.47 for Schizothorax plagiostomus, Schizothorax esocinus, Racoma labiata, Tor putitora, Barilius vagra, Garra gotyla, Puntius ticto and Arassius auratus respectively. Current study is the first attempt on the LWRs of cyprinid species, provide a baseline approach for conservation and /management of local fish fauna of economic importance.

Lernaeid parasites prevalence in commercial freshwater fish species at various fish farms in Pakistan

Reports abound on Lernaea parasitizing the brood stock, fingerlings, and marketable-sized culturable freshwater fish species in various parts of the world. We investigated seven small-scale aquaculture farms and how the prevailing Lernaea is impacting them. Randomly seven fish farms were selected to determine the prevalence percentage of lernaeid ectoparasites. Relevant information of the fishponds to estimate the various aspects such as effects of water source and quality, feed, stocking density, treatment used, and weight and length of fish, concerned with Lernaea infestation and prevalence was gathered. The results indicated that Catla catla (F. Hamilton, 1822) showed highest prevalence (41.7%) among the seven fish species, whereas Oreochromis niloticus showed zero. Other five fish species Ctenopharyngodon idella, Cirrhinus cirrhosis, Cyprinus carpio, Labeo rohita and Hypophthalmichthys molitrix showed 13.2%, 8.1%, 7.7%, 7.4%, 0.9% prevalence, respectively. In Royal Fish Farm 84.3% lernaeid infestation was observed, while no parasite was observed in the Vicent’s Chunnian fish farm. The water source, quality, feed, fertilizers, stocking density, water temperature, and potential treatment options displayed varying tendencies among fish farms and prevalence. Depending on the weight and length, the highest prevalence (56.7%, and 66.7%) was observed in 3501-4000 g and 81-90 cm groups. The infestation rate varied in various fish body parts with the dorsal fin the most vulnerable organ and showed 2.3% overall prevalence (while 18.4% contribution within total 12.6% infestation). Out of 147 infected fish samples, 45 were extensively contaminated by Lernaea spread. In conclusion, our findings confirm that Lernaea could pose a considerable threat to marketable fish, and various treatment options should be educated to the farmers to help mitigate the spread and potential losses. Furthermore, Catla catla is more vulnerable to Lernaea infestation (41.7%), so are the fish species being cultured at higher stocking densities.

Length-weight relationships of fish from sandy beaches

This study estimated the length–weight relationships of 16 fish species occurring close to the shores of sandy beaches along the lower Negro River basin, Brazilian Amazon. The specimens were captured for one day each month, in October to November 2016, early in the morning and early evening, using trawl net (20 m taken for standard length (SL – 0.1 cm precision) and total weight (TW – 0.01 g precision). The parameters a and b of the equation WT = a.LTb were estimated. The a values ranged from 0.0018 to 0.0226 and b values ranged from 2.5271 to 3.3244. This study also provides new data on of maximum lengths for six species, Amazonsprattus scintilla, Brycon pesu, Moenkhausia megalops, Pachyurus paucirastrus, Reganella depressa and Trachydoras microstomus, and new reports of the LWRs parameters of 15 fish species.

Anatomical and histological traits of Brycon amazonicus liver cultivated in a semi-intensive system

The work aimed to evaluate the weight-length relationship and the condition factor, characterizing the biometry, anatomy, histology and volumetric density of the liver of Brycon amazonicus, in different stages of body growth. The experiment used twenty specimens in four stages of body growth (PI, PII, PIII and PIV) harvested every 90 days, containing five specimens, each group. The livers were dissected, weighed (g) and processed routinely using the hematoxylin and eosin technique. The data were submitted to analysis of variance, Pearson’s correlation test and linear regression. The equation that represented the weight-length relation was W = 0.05902 x L2.63, with negative allometric growth, but with a relative condition factor equal to 1.0. The liver was divided into three lobes with the gallbladder close to the right lobe and its color varied from light red to dark red, not varying in relation to other fish species. The hepatosomatic relationship followed body growth until the PII group stage and then declined, demonstrating the behavior of its development in Brycon amazonicus. The organ consists predominantly of hepatocytes, followed by sinusoidal vessels and capillaries, with histological morphology similar to that of many species of fish. Melanomacrophage centers were found only in the most developed animals, but in small quantities, prompting the development of new research on this cell, in this species. In this way, research of this nature allows the characterization of fish species, helping to improve breeding methods, understanding pathological processes caused by diseases, and obtaining better productive capacity, serving an increasingly demanding and prosperous market.

Mapping and Assessing Commercial Fisheries Services in the Lithuanian Part of the Curonian Lagoon

The spatial distribution of biomass of main commercial fish species was mapped to estimate the supply of a provisioning fishery service in the Curonian lagoon. Catch per unit effort (CPUE) was used as a proxy to estimate the efficiency of commercial fishing and, subsequently, the potential biomass of fishes. The relationship between distinctive characteristics of the fishing areas and corresponding commercial catches and CPUE was analyzed using multivariate analysis. The total catch values and CPUE used in the analyses were derived from the official commercial fishery records. RDE analysis was used to assess the variation of both catch and CPUE of commercial fish species, while the percentages of bottom sediment type coverage, average depth, annual salinity, and water residence time in each of the fishing squares were used as explanatory variables. This distance e-based redundancy analysis allowed for the use of non-Euclidean dissimilarity indices. Fisheries data spatial distribution map indicated the lack of coherence between the spatial patterns of commercial catches and CPUE distribution in the northern part of the lagoon. Highest CPUE values were estimated in the central-eastern part of the lagoon as compared to the western part of the lagoon where CPUE values were substantially lower. Both total catch and CPUE appeared not to be related to the type of bottom habitats statistically while being spatially correlated in-between. However, the impact of salinity and water residence time calculated using the 3D hydraulic circulation model on the distribution of both CPUE and commercial catches was statistically significant.

Automated Detection, Classification and Counting of Fish in Fish Passages With Deep Learning

The Ocean Aware project, led by Innovasea and funded through Canada's Ocean Supercluster, is developing a fish passage observation platform to monitor fish without the use of traditional tags. This will provide an alternative to standard tracking technology, such as acoustic telemetry fish tracking, which are often not appropriate for tracking at-risk fish species protected by legislation. Rather, the observation platform uses a combination of sensors including acoustic devices, visual and active sonar, and optical cameras. This will enable more in-depth scientific research and better support regulatory monitoring of at-risk fish species in fish passages or marine energy sites. Analysis of this data will require a robust and accurate method to automatically detect fish, count fish, and classify them by species in real-time using both sonar and optical cameras. To meet this need, we developed and tested an automated real-time deep learning framework combining state of the art convolutional neural networks and Kalman filters. First, we showed that an adaptation of the widely used YOLO machine learning model can accurately detect and classify eight species of fish from a public high resolution DIDSON imaging sonar dataset captured from the Ocqueoc River in Michigan, USA. Although there has been extensive research in the literature identifying particular fish such as eel vs. non-eel and seal vs. fish, to our knowledge this is the first successful application of deep learning for classifying multiple fish species with high resolution imaging sonar. Second, we integrated the Norfair object tracking framework to track and count fish using a public video dataset captured by optical cameras from the Wells Dam fish ladder on the Columbia River in Washington State, USA. Our results demonstrate that deep learning models can indeed be used to detect, classify species, and track fish using both high resolution imaging sonar and underwater video from a fish ladder. This work is a first step toward developing a fully implemented system which can accurately detect, classify and generate insights about fish in a wide variety of fish passage environments and conditions with data collected from multiple types of sensors.