Ability of machine learning models to identify preferred habitat traits of a small indigenous fish (Chanda nama) in a large river of peninsular India.

Physical and chemical parameters of river influence the habitat of fish species in aquatic ecosystems. Fish showed a complex relationship with different aquatic factors in river. Machine learning modeling is a useful tool to identify relationships between components of a complex environmental system. We identified the preferred habitat indicators of Chanda nama (a small indigenous fish), in the Krishna River located in peninsular India, using machine learning modeling. Using data on Chanda nama fish distribution (presence/absence) and associated ten physical and chemical parameters of water at 22 sampling sites of the river collected during the year 2001-2002, machine learning models such as random forest, artificial neural network, support vector machine, and k-nearest neighbors were used for classification of Chanda nama distribution in the river. The machine learning model efficiency was evaluated using classification accuracy, Cohen's kappa coefficient, sensitivity, specificity, and receiver-operating-characteristics. Results showed that random forest is the best model with higher classification accuracy (82%), Cohen's kappa coefficient (0.55), sensitivity (0.57), specificity (0.76), and receiver-operating-characteristics (0.72) for prediction of the occurrence of Chanda nama in the Krishna River. Random forest model identified three preferred physicochemical habitat traits such as altitude, temperature, and depth for Chanda nama distribution in Krishna River. Our results will be helpful for researcher and policy maker to understand important physical and chemical variables for sustainable management of a small indigenous fish (Chanda nama) in a large tropical river.

Tillage and crop establishment effects on weeds and productivity of a rice-wheat-mungbean rotation.

Weeds are one of the key threats in sustaining the productivity of the rice-wheat cropping system in the Indo-Gangetic Plains. The development of sound integrated weed management technologies requires knowledge of mechanisms that influence weed flora composition and weed seedbank dynamics. A long-term study was initiated in 2015 at Patna, Bihar, India to evaluate the effect of seven tillage and crop establishment methods on weed density, weed seedbank composition, and crop productivity in rice-wheat-mungbean rotation. All the treatments included zero-till mungbean after wheat. Tillage and crop establishment methods had differential effects on weed and weed seedbank composition. In rice, zero-till direct-seeded rice recorded 62% lower emergence of Cyperus iria, 82-90% of Echinochloa colona, and 81-83% of total weeds compared to tilled systems, but the system of rice and wheat intensification favoured E. colona. In wheat, the system of wheat intensification favoured the Phalaris minor and Solanum nigrum. Zero-till rice and wheat reduced the seedbank of Trianthema portulacastrum by 95%, and total weed seedbank by 62% compared to the system of rice and wheat intensification. Nearly, 72% of C. iria seeds, 62% of grasses, and 64% of broad-leaved weeds were in 0-15 cm soil layer. Zero-till direct-seeded rice produced a 13% lower rice grain yield than conventional puddled transplanted rice. Compared to the system of wheat intensification, zero-till wheat under triple zero-till systems produced an 11.5% higher grain yield. Managing weed seedbank is a long-term endeavour. The present study revealed that tillage and crop establishment methods influence weed density and diversity. Under zero-till rice-wheat system, rice yield decreases marginally, but the system productivity maintains due to improvement in succeeding wheat yield. This system is also helpful in reducing the weed flora density and soil weed seedbank. Regular monitoring and management of emerging pests such as armyworm (Mythimna separata) are, however, required. The study suggests that the adoption of triple zero-tillage can be a viable option for reducing the weed density and weed seedbank concurrently increasing the system productivity of the rice-wheat-mungbean cropping system in eastern Indo-Gangetic Plains.

Arsenic Bioaccumulation and Identification of Low-Arsenic-Accumulating Food Fishes for Aquaculture in Arsenic-Contaminated Ponds and Associated Aquatic Ecosystems.

Arsenic-contaminated food including farmed fish is one of the main routes of human exposure. Fish farmed in contaminated environment accumulates arsenic in different tissues with great variability. Thus, it is utmost important to quantify the risk associated with different farmed fish species in arsenic-contaminated aquaculture systems. In the present study, arsenic content was measured in twelve fish species (Labeo rohita, L. catla, Cirrhinus mrigala, Oreochromis niloticus, O. mossambicus, Liza tade, Puntius javanicus, L. calbasu, Glossogobius giuris, Macrobrachium rosenbergii, Ctenopharyngodon idella, and Bellamya bengalensis (gastropod)) collected from arsenic-contaminated aquaculture systems. Among the studied finfishes, C. idella was found to accumulate the lowest amount of arsenic (< 0.05 ± 0.00 mg kg-1) whereas the highest accumulation was noticed in O. mossambicus (1.0 ± 0.18 mg kg-1). However, the estimated carcinogenic and non-carcinogenic risks of human were found to be low for all the studied fishes. The calculated target hazard quotient (THQ) value for adults ranged from 0.01 to 0.08 whereas for children it ranged from 0.05 to 0.27 for low-arsenic-accumulating fishes (arsenic conc. < 0.5 mg kg-1). Based on these findings, C. mrigala, C. idella, and M. rosenbergii could be recommended as the candidate species for aquaculture in the arsenic-contaminated areas as farming of the low-arsenic-accumulating food fishes would also lower the risk of human exposure through food chain.

Metagenomic Analysis Reveals Bacterial and Fungal Diversity and Their Bioremediation Potential From Sediments of River Ganga and Yamuna in India.

In this study, we report the presence of a microbial community of bioremediation potential in terms of relative abundance and taxonomic biodiversity in sediment samples of river Ganga and Yamuna, India at nine different sites. Metagenomic libraries were constructed using TruSeq Nano DNA Library Prep Kit and sequenced on NextSeq 500 by Illumina Next Generation Sequencing (NGS) technology. Bioremediation bacteria belong to 45 genera with 92 species and fungi belong to 13 genera with 24 species have been classified using Kaiju taxonomical classification. The study revealed that Proteobacteria was the most dominant bacterial flora, followed by Actinobacteria, Firmicutes, and Deinococcus-Thermus. PCA analysis revealed that bioremediation bacteria viz. Streptomyces bikiniensis, Rhodococcus qingshengii, Bacillus aerophilus, Pseudomonas veronii, etc., were more dominant in highly polluted river stretch as compared to less polluted river stretch. Similarly, the relative abundance of bioremediation fungi viz. Phanerochaete chrysosporium and Rhizopus oryzae, etc., were significantly correlated with the polluted Kanpur stretch of river Ganga. Several protein domains, which play a pivotal role in bioremediation in the polluted environments, including urea ABC transporter, UrtA, UrtD, UrtE, zinc/cadmium/mercury/lead-transporting ATPase, etc., were identified using protein domain analysis. The protein domains involved in pesticide biodegradation viz. P450, short-chain dehydrogenases/reductases (SDR), etc., were also discovered in river sediment metagenomics data. This is the first report on the richness of bioremediation microbial communities in the Ganga and Yamuna riverine ecosystems, highlighting their importance in aquatic pollution management.

Metagenome analysis from the sediment of river Ganga and Yamuna: In search of beneficial microbiome.

Beneficial microbes are all around us and it remains to be seen, whether all diseases and disorders can be prevented or treated with beneficial microbes. In this study, the presence of various beneficial bacteria were identified from the sediments of Indian major Rivers Ganga and Yamuna from nine different sites using a metagenomic approach. The metagenome sequence analysis using the Kaiju Web server revealed the presence of 69 beneficial bacteria. Phylogenetic analysis among these bacterial species revealed that they were highly diverse. Relative abundance analysis of these bacterial species is highly correlated with different pollution levels among the sampling sites. The PCA analysis revealed that Lactobacillus spp. group of beneficial bacteria are more associated with sediment sampling sites, KAN-2 and ND-3; whereas Bacillus spp. are more associated with sites, FAR-2 and ND-2. This is the first report revealing the richness of beneficial bacteria in the Indian rivers, Ganga and Yamuna. The study might be useful in isolating different important beneficial microorganisms from these river sediments, for possible industrial applications.

Impact assessment of barge trafficking on phytoplankton abundance and Chl a concentration, in River Ganga, India.

Impact of barge movement on phytoplankton abundance and biomass was assessed in the lower stretch of river Ganga, popularly known as Bhagirathi-Hooghly river, during April 2016 to March, 2017. Based on the magnitude of tide, intensity of shipping and boating activities, the stretch from Baranagar to Lalbag (278 km), located at latitude (22°38'33.41"N to 24°10'59.75"N) and longitude (88°21'21.29"E to 88°16'5.65"E) was divided into three zones viz. zone-I (Baranagar to Barrackpore), zone II (Triveni to Balagarh) and zone III (Nabadweep to Lalbag). Water samples were collected randomly from six stations covering 22 barge movements at their passage at three different time intervals viz., 30 minutes before 'barge movement', during 'barge movement' and 30 minutes after 'barge movement'. Analysis revealed the presence of 52 phytoplankton taxa belonged to 5 phylum during the study period. The abundance of phytoplankton was highest in zone-I followed by zone III and the zone II. A 44% decrease (1,997 ±1,510 ul-1) in phytoplankton abundance was observed during 'barge movement' with respect to normal condition (3,513 ± 2,239 ul-1) which could be due to propeller turbulence in the passage. Cell damage study revealed 21% damage in phytoplankton cell structure in 'during barge' followed by 'after barge' (10%) condition compared to natural state (6%). Study revealed that phytoplankton biomass (Chlorophyll a) was influenced by 'barge movement' in the sampling stretches and the impact was assessed by one way ANOVA. The effect was found significant at Barrackpore (p <0.01), Triveni (p <0.01), Balagarh (p <0.01) and Lalbag (p <0.01) where as it was insignificant at Baranagar and Nabadweep, which may be due to continuous and existing boat trafficking at Baranagar and Nabadweep. Two way ANOVA computed using 'barge movement' and sampling stations showed significant (p<0.01) effect on magnitude of Chl a concentrations in the sampling locations. Thus, the 'barge movement' influenced phytoplankton abundance and biomass, it had a detrimental effect on phytoplankton cell architecture also. The data set of this work serves as foundation information to understand the ecological implications augmented barge induced environmental disturbances in waterways. This is the first such study which depicts the impact of 'barge movement' on aquatic food chain linkages in Bhagirathi- Hooghly river.

Expression patterns of heat shock protein genes in Rita rita from natural riverine habitat as biomarker response against environmental pollution.

River pollution is one of the principal environmental concerns and biomonitoring tools can play an important role in pollution assessment in the riverine environment. Heat shock proteins (Hsps) have been found to be suitable tools for monitoring stress response. In the present study, expression analyses of hsp genes (hsp27, hsp47, hsp60, hsp70, hsc70, and hsp90) and selected hsp-regulatory genes (hsf1, hyou1, ask1, jnk) were carried out by RT-qPCR in catfish Rita rita collected from selected stretches of river Ganga to investigate changes in their expression patterns as biomarker response. Water quality characteristics were measured in terms of physico-chemical characteristics (DO, BOD, COD, pH, conductivity), element profile (arsenic, mercury, cadmium, lead, chromium, zinc, copper) and persistent organic pollutants (POPs; HCH, DDT, aldrin, endosulphan, heptachlor). Water quality index was calculated and sampling sites were categorized as good/medium/bad. Multivariate analysis was carried out taking the water quality parameters and the fold changes in hsp gene expression as variables, which showed that hsp47 and hsp70b correlated well with BOD, an indicator of organic pollution. To identify the organic pollutant(s) which could be influencing the expression of hsps, again multivariate analysis was employed taking concentration of POPs and fold changes of hsps, which showed up-regulation of hsp47 and hsp70b (HSP72i) correlated well with concentrations of aldrin and HCH. Synergistic effects of these POPs could be responsible for the up-regulation of said hsps, although individually present in low concentration; thus, indicating synergistic effect of the POPs on hsp47 and hsp70b up-regulation as biomarker response.