1.  Introduction

The Bhima River, a major tributary of the Krishna River, originates from the Bhimashankar hills in Maharashtra and flows across the Deccan plateau, passing through diverse agro-ecological zones before merging with the Krishna River in Karnataka. Spanning approximately 861 kilometers, it plays a vital role in sustaining the socio-economic and ecological fabric of the regions it traverses. The river not only supports agricultural irrigation and urban water supply but also provides a critical habitat for a variety of aquatic organisms, ranging from microscopic planktonic species to large invertebrates and fish.

Understanding the biological constituents of freshwater ecosystems is imperative for assessing their ecological health. Microfauna such as protozoa, rotifers, and cladocerans, along with macrofauna like molluscs, annelids, and arthropods, form essential links in aquatic food webs. They influence nutrient cycling and sediment dynamics and serve as primary indicators of water quality due to their sensitivity to environmental changes [1]. The Bhima River basin, with its mix of urban-industrial and agricultural influences, provides a unique ecological landscape where faunal diversity patterns can reveal critical insights into freshwater ecosystem functioning and anthropogenic pressures.

Despite the ecological importance of the Bhima River, research on its biodiversity—particularly the integration of both micro and macrofaunal data—is surprisingly limited. Most available studies focus on isolated groups such as fish [2] or molluscs (Waghmare et al., 2012), without capturing the comprehensive faunal dynamics that define the river’s biotic integrity. In addition, the lack of seasonal and spatial monitoring frameworks limits our understanding of temporal fluctuations in biodiversity and the factors driving them. There is also a gap in using faunal data to develop predictive ecological indicators or region-specific water quality assessment tools.

The present study aims to bridge these gaps by conducting an integrated assessment of both micro and macro fauna along selected stretches of the Bhima River. The objectives of this study are:

• to identify and document the diversity of micro and macrofauna in the Bhima River,
• to analyze seasonal and spatial variations in their composition,
• to correlate faunal patterns with physicochemical water parameters, and
• To identify potential bioindicator species for long-term ecological monitoring.

By addressing these objectives, the study contributes to building a foundational knowledge base that can aid in sustainable river management and conservation planning across riverine systems in India. The findings are also expected to support the formulation of biotic indices tailored for the ecological conditions of the Bhima basin.

2.  Literature Review

2.1  Previous Studies on Microfauna in Indian Rivers

Microfauna, including protozoa, rotifers, and microscopic crustaceans, play a foundational role in freshwater ecosystems by contributing to primary production, nutrient recycling, and trophic dynamics. In Indian contexts, research on microfaunal diversity has predominantly focused on larger river systems such as the Ganga and Yamuna, while studies on regional rivers like the Bhima remain scarce. As a research provided a nationwide overview of freshwater biodiversity, highlighting the underrepresentation of microfaunal surveys in smaller basins. Studies acknowledged that although the Bhima basin is ecologically active, micro-level biological assessments are fragmented and mostly qualitative.

2.2  Macrofauna Biodiversity Trends and Threats

Macrofauna, including molluscs, annelids, and arthropods, serve as crucial components of benthic communities and respond distinctly to habitat alterations. Research in Maharashtra by Waghmare et al. (2015) linked molluscan diversity in Bhima River segments to localized pollution levels, particularly near pilgrimage towns like Pandharpur. Similarly, fish diversity assessments by Kumbhar and Kumbhar (2018) at Gursale and Kumbhar et al. (2024) at Machnur identified site-specific richness patterns affected by anthropogenic pressure. However, a major gap remains in integrating these macrofaunal findings with simultaneous microfaunal data or water quality indices.

2.3  Water Pollution Impacts on Aquatic Biodiversity

Pollution—especially from untreated sewage, industrial effluents, and agricultural runoff—has significantly altered aquatic biodiversity in Indian rivers. Sayyad (2014) noted bioaccumulation of heavy metals in crabs from the Godavari River, revealing health risks that parallel similar undocumented threats in the Bhima system. Soni et al. (2013) highlighted how reduced environmental flows exacerbate ecological stress in monsoonal rivers, which applies equally to the Bhima during dry months when discharge is minimal. Waghmare et al. (2015) further correlated mollusc depletion with rising biochemical oxygen demand (BOD), indicating that aquatic fauna can effectively mirror pollution severity.

2.4  Use of Fauna as Bioindicators

Aquatic fauna, particularly invertebrates, have long been recognized as sensitive indicators of ecological health. Macroinvertebrate-based indices such as the BMWP and ASPT scores have been tested in Indian rivers, though their local calibration remains limited. Binoy and Prasanth (2016) demonstrated ecosystem-driven dietary shifts in climbing perch, hinting at behavioral bioindicator potential. Trivedi et al. (2016) recorded intersex conditions in freshwater crabs (Barytelphusa cunicularis), attributing them to hormonal disruption likely caused by pollutants—an example of faunal response acting as an early warning for ecosystem stress.

2.5  Gaps Identified from Prior Research

The reviewed literature indicates four major gaps:

• limited microfaunal surveys in rivers like Bhima,

• lack of integrated micro-macro studies using synchronized water quality data,
• underuse of native bioindicator models in routine assessments, and
• Inadequate long-term biodiversity monitoring. While broader reviews such as that by Subramanian et al. (2018) emphasize national trends, river-specific assessments remain patchy. Moreover, earlier studies often overlook seasonal variation and habitat heterogeneity, reducing their effectiveness in shaping ecological policy or conservation frameworks.

3.  Materials and Methods

3.1  Study Area

The Bhima River originates from the Bhimashankar hills in the Western Ghats of Maharashtra (19.07°N, 73.53°E) and flows southeast through Solapur and Pune districts before entering Karnataka, ultimately joining the Krishna River. It covers diverse ecological zones, ranging from forested catchments to urban-industrial belts and intensively cultivated farmlands. This hydrological and ecological diversity makes the river a suitable model for assessing spatial variation in aquatic fauna.

3.2  Sampling Sites and Selection Criteria

Six representative sites were selected to capture ecological variation across urban, rural, and semi-urban gradients. The selection was based on accessibility, hydrological continuity, and anthropogenic disturbance levels such as sewage discharge or agricultural runoff.

3.3  Microfauna Collection Methods

Microfauna were sampled using standard plankton nets (20 µm mesh) through horizontal and vertical hauls. Surface water (20–30 cm depth) was filtered and preserved in 4% formalin. The preserved samples were later transferred to laboratory conditions for quantitative and qualitative analysis.

• Macrofauna Collection Methods

Macrofauna were collected by handpicking from river substrates, using dredging nets (mesh 500 µm), and baited traps in shallow zones. Sampling spanned 1 m² quadrats along three transects per site. Each sample was cleaned on-site and preserved in 70% ethanol for laboratory analysis.

3.5  Water Quality Analysis

In situ water quality was measured at each site using handheld multiparameter meters. Parameters included temperature, pH, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS), and nitrates.

3.6  Species Identification Protocols

Microfaunal identification was performed under a compound microscope using standard freshwater plankton keys (APHA, 2005). Macrofauna were identified using morpho-taxonomic guides provided by the Zoological Survey of India (Subramanian et al., 2018) and recent literature. Only specimens with complete morphological integrity were considered for inclusion in data analysis.

3.7  Statistical Tools and Biodiversity Indices

Biodiversity indices such as Shannon-Weaver Index (H’), Simpson’s Diversity Index (D), and Margalef’s Richness Index were calculated using PAST (Paleontological Statistics) version 4.03. One-way ANOVA and Pearson correlation were applied to test seasonal and spatial differences across sampling sites. Hierarchical clustering was performed to explore ecological groupings based on faunal presence and physicochemical similarity.

4.  Results

4.1  Microfauna Composition and Abundance

Microfaunal analysis revealed a total of 34 species, distributed among five major taxonomic groups: rotifers, cladocerans, copepods, protozoans, and ostracods. Rotifers were the most dominant group across all sites, particularly at Pandharpur (S2) and Shirpur (S3), suggesting moderate organic enrichment. Copepods were prevalent in cleaner rural sites like Thigaon (S1) and Afzalpur (S5), indicating better oxygenation levels

4.2  Macrofauna Species Observed and Seasonal Variation

Macrofauna assessments revealed 28 species, mainly from the Mollusca, Annelida, and Arthropoda phyla. Rural sites like S1 and S5 recorded more gastropods and oligochaetes, whereas S2 and S4 had reduced diversity due to urban runoff and industrial pollution. Seasonal variation was evident, with monsoon months showing reduced macrofaunal abundance due to high turbidity and water flow.

4.3  Biodiversity Indices (Shannon-Weaver, Simpson, etc.)

Biodiversity indices calculated for both micro and macro fauna showed the highest Shannon-Weaver Index (H’) values at Thigaon (2.89) and Afzalpur (2.73), while urban sites like S2 and S4 recorded lower values (<2.0), indicating ecological stress. Similar patterns were observed for Simpson’s Diversity Index and Margalef’s Richness Index.

4.4  Correlation of Faunal Presence with Water Quality

Correlation analysis between species richness and water parameters revealed significant relationships:

• Positive correlation between DO and both microfaunal and macrofaunal diversity (r = 0.82, p < 0.01)

• Negative correlation between BOD and faunal richness (r = -0.79, p < 0.01)

4.5  Indicator Species Identification

Certain species were identified as potential bioindicators:

• Brachionus calyciflorus (rotifer) and Dugesia sp. (flatworm) were abundant in moderately polluted waters (S2, S3)

• Chironomid larvae and Tubifex tubifex (oligochaete) were dominant in high BOD areas, marking them as pollution-tolerant species

• Lymnaea acuminata and Physa sp. were found only in low-disturbance sites, suggesting use as indicators of good water quality

To visualize community structure at each site, a stacked bar chart was developed (Figure 5), revealing rotifers dominated S2 and S3, while copepods contributed significantly in S1 and S5, indicating cleaner habitat preference

This figure complements Table 4 by highlighting which group dominates at each location in percentage terms, showing ecological balance or imbalance.

Summarizes multi-metric biodiversity data across locations, improving interpretability of ecological health.

Statistical overview of correlations between faunal richness and physicochemical parameters (DO, BOD, pH, etc.)

 

Supports the claims made in Section 4.4, validating species-environment relationships.

A correlation matrix (Figure 7) further confirmed the negative relationship between BOD and species richness, while DO had a strong positive correlation with both micro and macrofaunal indices.

All sites show variations in water quality, such as DO and BOD, over different seasons. This information is particularly useful before or after Figure 3, as it links seasonal patterns in water quality to variations in biodiversity.

As shown in Figure 8, BOD levels peaked during the monsoon due to surface runoff, aligning with the observed decline in species richness in that season.

5.  Discussion

The present study provides an integrated assessment of micro and macro faunal diversity along various segments of the Bhima River, establishing a clear relationship between biodiversity patterns, water quality parameters, and anthropogenic activities. The findings reaffirm earlier research while offering novel insights specific to the Bhima basin, particularly by unifying microfaunal and macrofaunal analyses across spatial and seasonal gradients.

5.1  Interpretation of Findings and Literature Comparison

The dominance of rotifers and copepods among microfauna aligns with studies conducted in the Godavari and Krishna basins, where similar planktonic communities were indicative of moderate organic enrichment and high productivity (Subramanian et al., 2018; Binoy & Prasanth, 2016). Macrofaunal trends, particularly the presence of molluscs and oligochaetes in less disturbed sites (S1, S5), corroborate earlier observations by Waghmare et al. (2015), who reported higher benthic diversity in rural stretches of Bhima due to stable substrate and minimal pollution.

The seasonal drop in species count during the monsoon season (Figure 3) is ecologically consistent with flood-driven displacement and turbidity-induced stress (Sayyad, 2014). This supports the broader consensus that high discharge periods result in habitat disruption, reducing faunal colonization and visibility.

5.2  Factors Driving Spatial and Temporal Variation

Spatial variation in faunal diversity was primarily governed by dissolved oxygen (DO) and biochemical oxygen demand (BOD), as evidenced by correlation values in Table 6 and Figure 7. Rural sites showed better oxygenation due to riparian vegetation and minimal effluent discharge, supporting higher diversity indices (Figure 6). In contrast, sites like Pandharpur (S2) and Mohol (S4) reflected the ecological consequences of urbanization and ritual wastewater, with lower diversity and the appearance of pollution-tolerant species such as Chironomus larvae and Tubifex tubifex.

Seasonal trends also influenced community structure, as confirmed by Figures 8A and 8B, where BOD levels spiked during the monsoon, disrupting benthic habitats and reducing fauna stability. Post-monsoon recovery in both DO and species counts illustrates the river’s partial resilience but also underscores the need for seasonal management strategies.

5.3  Ecological Role and Bioindicator Significance

The distribution of species such as Lymnaea acuminata, Daphnia magna, and Brachionus calyciflorus offers strong evidence for their role as bioindicators in the Bhima system. These organisms are known to respond rapidly to changes in nutrient load and oxygen levels (Trivedi et al., 2016; Kumbhar & Kumbhar, 2018), and their localized abundance in certain sites supports their diagnostic relevance.

The use of both micro and macro indicators allows for more robust ecological assessment frameworks. For example, Figure 4 reveals how species assemblages cluster under different pollution loads, offering a pathway to develop Bhima-specific biotic indices in the future.

5.4  Broader Ecological and Conservation Implications

This study highlights the need for integrated biodiversity assessments in regional rivers that are often overlooked in favor of larger systems. The Bhima River, despite being a crucial tributary of the Krishna basin, lacks routine ecological monitoring—a gap this research begins to fill. The correlation between faunal decline and urban pollution is not unique to Bhima and reflects broader patterns seen in other monsoonal rivers such as the Yamuna (Soni et al., 2013) and Godavari (Sayyad, 2014).

Without immediate conservation strategies, particularly in urban reaches, the risk of faunal homogenization and biodiversity loss remains high. Introducing biomonitoring protocols based on indicator species identified here can inform policy interventions under state and central river management schemes.

6.  Practical Implications

The findings of this study provide critical groundwork for applying biological data in freshwater monitoring, conservation planning, and environmental policymaking. By integrating species-based assessments with water quality parameters, this research supports the development of practical, field-applicable tools for safeguarding riverine ecosystems like the Bhima.

6.1  Faunal Diversity as a Tool for Water Quality Monitoring

The correlation between species richness and key physicochemical variables such as dissolved oxygen (DO) and biochemical oxygen demand (BOD) confirms that micro and macrofauna serve as reliable bioindicators. For instance, the consistent presence of rotifers and copepods in less polluted stretches reflects oxygen sufficiency, while the dominance of tolerant oligochaetes like Tubifex in urban sites marks high organic loading (Waghmare et al., 2012; Trivedi et al., 2016). This establishes a biological baseline for cost-effective water quality surveillance, especially in regions with limited infrastructure for chemical monitoring.

6.2  Inputs for Conservation and Ecological Restoration

This study reveals biodiversity hotspots within the Bhima basin—particularly sites like Thigaon (S1) and Afzalpur (S5)—that can serve as reference zones for restoration. Protecting such regions while rehabilitating degraded sites like Pandharpur (S2) and Mohol (S4) can promote ecological recovery. Similar practices have been effective in the Godavari basin, where faunal reappearance was documented after effluent regulation (Sayyad, 2014). The identified indicator species offer measurable benchmarks to evaluate the success of riverbank restoration and habitat improvement initiatives.

6.3  Integration into Biotic Index Frameworks

The synthesis of biodiversity data and pollution gradients makes a strong case for developing Bhima-specific biotic indices. Existing tools like the Biological Monitoring Working Party (BMWP) score have limited applicability in tropical regions unless calibrated for local fauna (Subramanian et al., 2018). By including local species that are common and those that are sensitive to pollution—like Lymnaea acuminata, which indicates clean water, and Chironomus larvae, which can tolerate pollution—we can create a flexible biotic index that works well for rivers in India during the monsoon season.

6.4  Recommendations for Policy and Local Environmental Management

The integration of faunal indicators in state-level water governance policies—especially in Maharashtra and Karnataka—can enhance river monitoring efficiency. Policymakers are advised to:

• Mandate seasonal biological surveys along critical Bhima segments

• Establish a Bhima River Bioassessment Index based on suggested taxa.

• Engage local colleges and NGOs in data collection using simplified ID guides

• Incorporate biological criteria into Environmental Impact Assessments (EIAs) for riverfront developments

Such actions would align with India’s National Mission for Clean Ganga (NMCG) and broaden its applicability to tributaries like the Bhima.

7.  Conclusion

This study offers a comprehensive account of micro and macro faunal assemblages across ecologically distinct stretches of the Bhima River. By combining taxonomic assessment with water quality analysis, the research contributes a layered understanding of how faunal diversity mirrors environmental conditions across spatial and seasonal gradients. The integration of both microscopic and macrobenthic species allows for a nuanced evaluation of river health, positioning these biological components as practical tools for long-term ecosystem monitoring.

The key contribution of this work lies in demonstrating the bioindicator potential of site-specific species—such as Brachionus calyciflorus and Lymnaea acuminata—and validating their relationships with water quality variables like dissolved oxygen and organic load. Unlike earlier studies that focused narrowly on fish or individual macroinvertebrate groups (Waghmare et al., 2012), this study emphasizes the value of a multi-tiered faunal framework. It thereby strengthens the case for developing adaptive biotic indices suitable for Indian monsoon river systems, an area still underrepresented in ecological research (Subramanian et al., 2018).

While the findings are robust, certain limitations remain. The study was conducted across a single annual cycle and may not capture inter-annual variability or rare ecological disturbances such as drought or flooding. Moreover, taxonomic identification of microfauna was limited to morpho-taxonomic methods without molecular confirmation.

These limitations highlight the need for continuous, multi-year monitoring supported by both field-based taxonomy and molecular tools. Future research should focus on developing predictive models linking land use change to biodiversity loss, and on expanding bioassessment frameworks to include trophic relationships and ecosystem service evaluations.

Given the increasing anthropogenic pressure on mid-sized Indian rivers, this study underscores the urgency of integrating biological monitoring into environmental governance. By anchoring ecological knowledge in real-world water management, it offers a roadmap for sustainable river basin planning and conservation in rapidly transforming landscapes like the Bhima basin.

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