Muscling mussels: Understanding the invasive potential of the South American bivalve Mytella strigata (Hanley, 1843) in the Northern Indian Ocean.

In past decades, non-native species invasion has emerged as one of the leading drivers of biodiversity loss in terrestrial and aquatic ecosystems globally. In aquatic ecosystems, invasion by bivalve species has increased substantially due to their evolutionary resilience and adaptability. This study aimed to determine the habitat suitability of the South American bivalve Mytella strigata in the northern Indian Ocean using Species distribution modelling. The species occurrence and environmental data for model building were extracted from GBIF, Bio-ORACLE, The World Bank Data Catalogue and GMED. Pearson's correlation (<0.7) and Variance inflation factor (<10) analyses were used to select the environmental covariates. Individual models were built by combining the native range occurrence data of Mytella strigata with the bioclimatic data under the current climatic setting. Ten individual models were built and ensembled to create the final model using the biomod2 package. The variable importance score and the response curve plot were used to identify the most crucial variable and its influence on the models. Distance to port had the highest influence on predicting the distribution of Mytella strigata. The results indicated that the western coast of India as more susceptible to invasion. Our predictions indicate that the species has the potential to become highly invasive in the region, given the vast habitat suitability and documented introduction and presence of the species in the region. This research generated baseline information on the habitat suitability of M. strigata that will aid in managing and restricting its spread in the region. Considering the substantial impact of the species in other introduced ranges worldwide, immediate action should be initiated for the swift management of M. strigata from the Indian coast.

Dynamics of environmental variables during the incidence of algal bloom in the coastal waters of Gujarat along the northeastern Arabian Sea.

The dynamics of physico-chemical, nutrient, and chlorophyll-a variables were studied in the bloom and non-bloom locations along the off-Gujarat coastal waters to understand the variability in biogeochemistry using multivariate analytical tests. The dissolved oxygen was significantly lower in the bloom stations (3.89 ± 0.44 mgL-1) than in the non-bloom stations (5.50 ± 0.70 mg L-1), due to the biological degradation of organic matter in addition to anaerobic microbial respiration. Nutrients (PO4 and NO3) and Chl-a concentrations were recorded higher in the bloom locations at 0.83 ± 0.21 µmol L-1, 4.47 ± 0.69 µmol L-1, 4.14 ± 1.49 mg m-3, respectively. PO4 and NO3 have shown a significantly higher positive correlation of r = 0.73 and r = 0.69 with Chl-a for bloom data than the non-bloom data. The percentage variance contributed by PC1 and PC2 for both bloom and non-bloom locations were estimated at 52.33%. The variable PO4 explains the highest 24.19% variability in PC1, followed by Chl-a (19.89%). The PO4 triggers the bloom formation and also correlates to the higher concentrations of Chl-a in the bloom locations. The bloom concentration ranges from 9553 to 12,235 trichomes L-1. The bloom intensity has shown a significant positive correlation with Chl-a (r = 0.77), NO3 (r = 0.56), and PO4 (r = 0.30), but a negative correlation was noticed with DO (r = - 0.63) and pH (r = - 0.49). The study also initiates a way forward research investigation on ocean-color technologies to identify and monitor blooms and climate change-driven factors for bloom formation. The occurrence of bloom and its influence on fishery resources and other marine biotas will open many research windows in marine fisheries, oceanography, remote sensing, marine biology, and trophodynamics.