Materials and methods

2.1 Study Area

The Aganashini (lat. 14° 27' 53.6'' N to 14° 31’ 18.8” N, long. 74° 29' 26.8'' E to 74° 20’ 54.4” E) is a west flowing river from the central Western Ghats mountain ranges in the Uttara Kannada district of Karnataka state, India. The 121 km long river that has confluence with the Arabian Sea, a pristine and highly productive estuary, through which the high tide moves almost 27 km upstream, more in the driest months of summer (Boominathan et al. 2008). Totally eight lentic and lotic habitats (4+4) were chosen for sampling in this estuary based on the floral and faunal assemblages and water quality. Among these sampling locations, bat roosting site (BRS), egret roosting site (ERS), Kagal-oyster shell bed (KOSB) and mudflats (MF) fall under the estuarine lotic habitat whereas other four sites are lentic with prolonged water retention time despite diurnal tidal variations. The lentic sites are estuarine rice fields (gaznis) and are separated from the estuary by embankments fitted with sluice gates (for adjusting the water levels) and are always in submerged/water-logged state (Fig. 1).
The gaznis chosen for study are the ones modified for shrimp farming, a digression from the traditional rice cropping. Mangroves, both natural formations, and planted ones of different ages are widespread all over the intertidal zones. A detailed description of each of the study site with its habitat type and characteristic flora and fauna associated site-wise is given in Table 1.

Fig. 1 Aganashini Estuary with Different Habitats considered for the Study

Table 1. Description of the Study Site

2.2 Diatom and Water Sampling

Water and biological samples (triplicates) were collected during pre-monsoon season (March-April 2017). Epipelic diatoms (attached to the sediments) were collected from all the stations, during low tide hours (for easy accessibility), using a thin spatula, from the sediment surface not deeper than 0.5 cm depth, to ensure the sampling of only live and motile diatoms. The samples were immediately fixed with Lugol’s iodine and processed in the laboratory following the standard protocols of (Taylor, Harding, et al. 2007). Acid digestion and pre-processing of diatom samples were carried out following KMnO4 and hot HCl method (Kelly & Whitton 1995). Species identification was done based on morphological features, following standard identification keys (Van Heurck 1896, Simonsen 1968, Patrick and Reimer 1966, Krammer and Bertolet 1986 and Karthick et al. 2013). Species richness and relative abundances across different sampling locations were determined by counting a maximum of 400 valves per sample following (Dares 2004) enumeration protocol. Onsite parameters analyzed during the study were air temperature (AT), water temperature (WT), pH, salinity and dissolved oxygen (DO). In-situ parameters of temperature, pH and salinity were measured using probes, whereas DO was measured chemically following Wrinkler’s method (APHA 2005). Nutrient analysis of water samples was carried out in the laboratory. Nitrates (NO3-), phosphates (PO43-) and reactive silicates (SiO44-) were analyzed following Standard Methods for Analysis of Water and Waste water (APHA 2005).

2.3 Diatom Tolerance, Sensitivity and Lipid Content

For more than four decades now, tolerance and sensitivity of diatoms have been assessed through various diatom indices considering the environmental parameters and its associated diatom community structure. Some noteworthy diatom based ecological studies (Kelly & Whitton 1995, Pan et al. 1996, Stevenson et al. 1996, Potapova & Charles 2002, Soininen & Eloranta 2004, Weilhoefer & Pan 2006, Potapova & Charles 2007, Chessman et al. 2007, Taylor, Prygiel, et al. 2007, Mitbavkar & Anil 2008, D’Costa & Anil 2010, Tan et al. 2014, Breuer et al. 2016, Hausmann et al. 2016 and Rath et al. 2018), which provide the details on the tolerance and sensitivity of a diatom species. These data integrated with the lipid content details would provide insights into lipid productivity potential of candidate diatom strains. An effort has been made to compile the information on diatom and environmental parameters with the lipid content (% dry cell weight). Multivariate analyses techniques such as canonical correspondence analysis, agglomerative hierarchical clustering, and regression analysis were performed by considering parameters like nutrients and other physical conditions as independent variables and lipid content as a dependent variable. This would also aid in lipid estimation indirectly, provided the physico-chemical parameters and the diatom distribution details of a location are known.

2.4 Statistical Analyses

One-way analysis of variance (ANOVA) carried out on diatom species composition and the nutrient (nitrate and silicate) levels showed significant differences in species composition with respect to nutrient levels with spatial variability across the study regions. Turkey’s pairwise comparison was performed to understand the level of significance of nutrient’s interaction with species richness. Shannon’s diversity and Simpson’s dominance indices were calculated for all the study locations. A multivariate hierarchical clustering analysis was performed to understand the similarities among the stations based on their environmental and biological parameters using Paleontological Statistics software PAST V 3.0 (Hammer et al. 2001). The influence of each environmental factors on species composition recorded from the present study was determined by multivariate ordination approach using canonical correspondence analysis in PAST V 3.0. Agglomerative hierarchical clustering carried out based on results of the present study as well as the literature integrated data was done using R Studio version 1.1.423. Multivariate regression modelling of the lipid content with Nutrients (Nitrates, Phosphates and Silicates), light intensity, salinity and p^H was performed using Vegan package in R Studio version 1.1.423. The regression showed significance at p <0.05 (Cumming 2013, Demirtas 2018) for all critical independent growth variables. Data cleaning for multivariate regression analysis was done by removing the scatter (average ± standard deviation) of physico-chemical parameters.