Assessing the trophic status of a tropical microtidal estuary applying TRIX and Random Forest - A combined approach

The present study assessed the trophic status of a medium-sized microtidal estuary, Rushikulya, India using a combination of mutimetric trophic indices (TRIX, TRBIX) and a machine learning approach (Random Forest). A total of 108 samples were considered to build a predictive model for chlorophyll a (Chl a) and 17 response water variables by observing two annual periods (2021-2023) at six sampling sites. Mean values of TRIX (5.04 ± 0.72) and TRBIX (0.17 ± 0.08) reflected that the estuary has a moderate degree of eutrophication with 'good' water quality and 'biomass saturated'. However, the threshold of TRIX represents a transition state from 'moderate' to 'high' eutrophic. Random Forest model reflected that no apparent association between Chl a and water turbidity above 30 NTU and eutrophication in the estuary fluctuated mainly due to PO43--P along with electrical conductivity. Linear statistical correlations showed high correlation between Chl a and conductivity and a negative correlation between Chl a and dissolved oxygen, unlike PO43--P.

Spatio-temporal variability in macroinvertebrate community structure and ecological health status of a tropical dynamic river-estuarine system, India: An integrated approach of multivariate analysis

River-estuarine ecosystems are under severe anthropogenic threat due to resource exploitation, transportation, sewage/industrial discharges, and pollutants from surrounding areas. Monitoring the water quality and biological communities is essential for assessing ecosystem health and sustainability. Present study integrated the ecological community data along with water quality analysis to understand the impact of anthropogenic pressures on benthic macroinvertebrates. Samples were collected from 10 locations (comprising of both rural and urban areas) for Benthic macroinvertebrates, physico-chemical and microbiological parameters along the lower stretch of the Bhagirathi-Hooghly river-estuarine (BHE) system during the post-monsoon seasons of 2020, 2021, and 2022. During the entire study period, a total of 5730 individuals from 54 families in 19 orders of 3 phylum of macroinvertebrate were recorded. Among them Thiaridae (27.1%) and Chironomidae (22.8%) were found to be the most abundant families. Based on the water quality data Cluster analysis and nMDS indicated two distinct groups of locations: Group-I with rural settings and Group-II with urban settings. Alpha diversity metrics showed higher diversity (2.817) and evenness (0.744) in rural locations (Group-I) compared to urban locations (Group-II). The overall saprobic score of the macroinvebrate data revealed Group-I (5.09) to be in good condition, while Group-II (4.95) showed moderately polluted conditions. Redundancy analysis (RDA) highlighted the correlation of pollution-tolerant species (Chironomidae, Culicidae) with high organic loads i.e., biochemical oxygen demand (BOD), chemical oxygen demand (COD) in Group-II. In contrast, Group-I locations exhibited positive correlations with Dissolved Oxygen (DO) and supported less pollution-tolerant organisms (Coenagrionidae, Dytiscidae). The study emphasizes the importance of integrated analysis of ecological community data and water quality parameters to assess the health status of river-estuarine ecosystems.

Advancing evaluation of bioassessment methods: A reply to Liu and Cao

A series of three papers was written about the development of multimetric indices (MMIs) using diatoms in rivers, streams and lakes for transcontinental surveys conducted by the United States Environmental Protection Agency. Stevenson et al. (2013) used the surface sediment diatom data from the 2007 National Lake Assessment to develop national scale site specific models for MMIs to account for natural variation in condition among sites. Liu and Stevenson (2017) also used the 2007 lakes data to evaluate performance of MMIs by grouping sites by ecoregions or typologies (naturally similar types of lakes defined by similarity in diatom species composition) with site specific metric models (SSMMs) that adjust metrics for natural variability among sites. Tang et al. (2016) used benthic diatom data from the 2008-2009 National River and Stream Assessment to develop SSMMs and MMIs by ecoregion and typology. All three studies showed that SSMMs improved performance of diatom MMIs by accounting for natural variation among sites. None of the studies provided consistent evidence that grouping sites by typologies produced better MMI performance than grouping sites by ecoregions. Liu and Cao (2018) criticized the Tang et al. (2016) paper for using means and standard errors to evaluate relative performance of MMI calculation methods at the site group scale, however, their criticism is incorrect. Actually, Tang et al. (2016) only used means to summarize and report relative performance of MMI calculation methods in the body of the paper. Tang et al. (2016) appropriately used non-parametric rank sum approaches to evaluate the probability that the multiple MMI calculations for separate site groups were the same for ecoregion (n = 9) and typology (n = 7) site groups. Liu and Stevenson (2017) used this same non-parametric approach for tests of lake diatom MMIs. Liu and Cao's (2018) concerns can be addressed by distinguishing between the goals and methods used for testing and evaluation of MMI calculation methods at the national and site-group scales. Tang et al. (2016) did not aggregate data across site groups to test MMI performance at the national scale because they were following standard EPA methods that develop separate MMIs for each site group. In conclusion, Liu and Cao (2018) misunderstood the MMI evaluation in Tang et al. (2016) and added no new information to this body of work, because all the concerns they raised were discussed in Liu and Stevenson (2017).