Linking Fish Population Biology with LULC Dynamics: A Comparative Assessment of Puntius sophore in the Ganga and Punpun Rivers, India

1. Introduction

Puntius sophore is a quintessential Small Indigenous Fish Species (SIFs) that characterises a vital yet increasingly vulnerable component of the freshwater ecosystems of the Indian subcontinent [1]. It is a freshwater to brackish-water species belonging to the Cyprinidae family. It is a freshwater to brackish-water fish belonging to the family Cyprinidae and is widely distributed across inland waters of South and Southeast Asia, including India, Pakistan, Nepal, Bangladesh, Myanmar, Bhutan, Afghanistan, and parts of China [2]. It is benthopelagic in nature, inhabiting the lower to middle layers of the water column in rivers, streams, ponds, floodplains and sub montane regions [3,4]. It is a shoaling fish, often found in groups, and can tolerate diverse riverbed substrates consisting of sand, mud, gravel, and cobbles. Millions of rural communities depend on it for their nutritional needs, including micronutrients like calcium, iron, vitamins and essential fatty acids [5,6]. As SIFs are often consumed whole, they provide a bioavailable source of nutrition that is particularly crucial for vulnerable groups such as women and children, helping to combat malnutrition [1]. It also has economic value as an ornamental item and is highly demanded in fresh and processed forms in domestic and international markets [7,8]. The riverine ecosystem, along with fish diversity and population structure of fish species, is adversely affected by human activities such as dam construction, pollution, change in land use patterns, invasion by exotic species, deforestation, climate change, and land erosion, ultimately leading to habitat loss and degradation [9]. Numerous freshwater fish species have become endangered, especially in the Ganges basin, where demand is extremely high. Despite its high value, the natural populations of P. sophore are fast declining mainly because of overfishing, habitat destruction, environmental degradation caused by pollution, diseases and introduction of exotic species [10,11]. Considerable study has already been done on the biological aspects of P. sophore, including length-weight relationships, condition factor [10,12,13,14,15,16], otolith studies [17], food & feeding and reproductive biology [1,2]. Usually, many inland fisheries are managed without stock assessments, despite the availability of data collection and computational capabilities. Only a few studies on the population dynamics of P. sophore have been documented globally. However, research has been conducted on other species within the same genus, such as P. sarana, in other rivers [18,19]. Study on inland fisheries stocks is considerably less than the marine fisheries, due to their highly seasonal and diverse characteristics, which hinder stock monitoring and management [20]. Data regarding the assessment of inland fish stocks has significance for the future production of inland fisheries [21].

The management of these fisheries is anticipated to provide quantitative information that could aid in decision-making on the sustainability of fishery exploitation and the development of suitable stocking and harvesting practices to improve fisheries [22]. To manage the stock successfully, it is essential to identify the factors contributing to the decline of fishing resources and comprehend the ecology of the species. Understanding a species' population dynamics makes maintaining and using it sustainably easier. Studies on population dynamics and LWRs are required to develop fisheries management strategies that maximise sustainable production. LWRs act as an essential functional metric to track population dynamics, overfished stocks, species viability in their natural environments, region-specific fish growth, feeding intensities, and catch estimation [23,24,25,26]. The major river systems of India exhibit great ecological and hydrological diversity, significantly impacting its fisheries from source to confluence. The Ganga basin's diverse habitats are home to 192 species of fish [27], essential to preserving ecological balance and supporting the livelihoods of the millions of people who depend on its resources. According to Vass et al. [28], the middle section of the Ganga River is a wide, advanced-stage river system that has historically been distinguished by a primarily sandy substrate and a high level of Ichthyofaunal variety. Therefore, the study has been designed to develop the baseline data on the population dynamics and length-weight relationships of P. sophore from the rivers Ganga and Punpun in India.

2. Materials and Methods

Study Area and Data Source

Fish samples were collected monthly from Digha ghat (25° 39' 11.959" N 85° 05' 39.535" E), Gai ghat (25°36'51.1"N 85°12'09.3"E), Gandhi ghat (25°37'25.2"N 85°09'50.9"E) along the river Ganga, as well as from Punpun ghat (25°30'06.5"N 85°06'04.2"E) Raj ghat (25° 29' 30.843" N; 85° 01' 10.671" E), Fatehpur ghat (25°29'53.8"N 85°11'41.5"E) along the river Punpun, during the period from February 2022 to January 2023 (Figure 1). Fishing was conducted by local fishermen employing a range of traditional gears, including set barrier nets (Khairel Jaal) characterized by a cod-end mesh size of 10–20 mm, a cod-end width of 1–1.5 m, an overall length of 6–8 m, and a width of 8–10 m; drift gill nets with a mesh size of 10–30 mm, measuring 80–100 m in length and 1.5–2 m in width; and cast nets. The fish species were identified using the taxonomic keys given by Jayaram [29]. The specimen's total length (TL) was measured using Vernier Caliper to the nearest 0.1 mm (from the tip of the snout to the lower caudal lobe) and weight was recorded using a digital balance to the nearest 0.01 g. For the stock parameter analysis, the average annual water temperature 27.8°C ± 0.47 was used.

Length-Weight Relationship (LWR)

The equation developed by Le Cren [30] was used for calculating the length-weight relationships (LWRs) of the fish:

$$W=a{L}^b$$

Here, 'W' signifies the total weight of the fish, 'L' indicates the total length of the fish, 'b' indicates the slope, and 'a' denotes the intercept of the length vs weight graph.

Condition Factors (K)

The condition factor (K) provides a rapid and precise value for assessing a fish's health regarding general wellness and fat reserve based on length and weight data. Fulton’s condition factor (Kc), relative condition factor (Kn) and allometric condition factor (Ka) were calculated using the following equations:

$$Kc=100\times \left({\displaystyle \frac{W}{T{L}^3}}\right)$$

$$Kn=Wo/Wc$$

$$Ka=100\times \left({\displaystyle \frac{W}{T{L}^b}}\right)$$

where W is body wet weight (BW, g), Wo is observed weight, Wc is calculated weight, b regression coefficient of LWRs and TL is total length (TL, cm) [30,31].

Growth, Mortality and LB-SPR Parameters Analysis

Stock parameters including growth, mortality, recruitment, and exploitation parameters, were estimated using TropFish-R (V 1.6.5) in the R package, following FAO standard stock assessment methodologies [32]. The pooled and monthly length-frequency data (LFQ) were utilized to estimate growth parameters. The Von Bertalanffy growth parameters (K, L∞, and t₀) were computed utilizing the Von Bertalanffy Growth Function (VBGF) on LFQ data in inbuilt ELEFAN programme in TropFish-R [33]:

$${\mathrm{L}}_{\mathrm{t}}={\mathrm{L}}_{\infty }\left[1-{\mathrm{e}}^{-\mathrm{K}\left(\mathrm{t}-{\mathrm{t}}_0\right)+\mathrm{s}\left(\mathrm{t}\right)-\mathrm{s}\left({\mathrm{t}}_0\right)}\right]$$

where Lt represents the length of a fish at age t, L∞ denotes the asymptotic length, k represents the growth rate coefficient in year⁻¹, and t₀ indicates the theoretical age of a fish at which length is zero. As tropical fishery s(t)- s(t₀), seasonal growth oscillation assumed to be minimum or zero in the present case [34]. The LFQ data were rebuilt by using a bin size of 3 mm and a moving average of 7. The initial L_∞ was calculated in package using formula L_{∞ =} L_max /0.95. The t_anchor is the parameters within the TropFish-R package to estimate fraction of year when VBGF curve crossed length zero for a particular cohort. For t_anchor the original seed value ranged from 0 to 1 [34]. Subsequently, ELEFAN using new optimization procedure simulated annealing (ELEFAN_SA) and genetic algorithm (ELEFAN_GA) were assessed to obtain growth parameters. The age at zero length (t₀) was calculated using the equation derived by Pauly [35], such as;

$${Log}_{10}\left(-{\mathrm{t}}_0\right)=-0.392-0.275{Log}_{10}\mathrm{L}\mathrm{\infty }-1.038{Log}_{10}\mathrm{K}$$

The growth performance index (φ’) was determined using the formula given by Pauly and Munro [36];

$$\mathsf{\phi }\mathrm{’}={Log}_{10}K+2{Log}_{10}L\mathrm{\infty }$$

The longevity (t_max) was calculated using Pauly’s [37] equation ${\mathit{t}}_{\mathit{m}\mathit{a}\mathit{x}}={\displaystyle \frac{3}{\mathit{K}}}+{\mathit{t}}_0$.

The natural mortality (M) was calculated using the equation proposed by Then et al. [38];

$$\mathrm{M}=4.118\times K^{0.73}\times {{\mathrm{L}}_{\mathrm{\infty }}}^{-0.33}$$

where K represents the growth constant and L∞ signifies the asymptotic length. The length-converted catch curve was employed to assess fishing mortality (F) and total mortality (Z), as per Pauly [37]. The exploitation ratio (E) was calculated using the formula proposed by Ricker [39], specifically E = F / Z.

The length-based SPR (LB-SPR) evaluates the impact of fish harvesting on a population's reproductive potential and it was calculated as mentioned by Hordyk et al. [40]

$$SPR={\displaystyle \frac{\sum ({1-Lx)}^{\frac{M}{K}\left\{\left(\frac{F}{M}\right)+1\right\}}\times {Lx}^b}{\sum ({1-Lx)}^{\frac{M}{K}}\times {Lx}^b}}$$

Where Lx is the fork length, M is the natural mortality rate, K is the von Bertalanffy growth parameter and F is the fishing mortality rate.

Recruitment Pattern and Virtual Population Analysis

The Virtual Population Analysis (VPA) for LFQ data was performed using Jones' length-converted cohort analysis [62], incorporating 'mortality' and the 'a' and 'b' parameters as input to the TropFish R program. Yield per recruit models (YPR), also known as prediction models (Thompson and Bell model 1934), evaluate stock status compared to reference levels and the impact of management controls like fishing effort regulations or gear selectivity. Biological Reference Point (BRP) was estimated using the technique proposed by Gulland [41], which assumes that Fopt (optimum fishing mortality) equals M.

Spatial Analysis

The present study utilized multi-temporal satellite datasets from the Landsat series (e.g., Landsat 5 TM for 1990/2000, Landsat 7 ETM+ for 2010, and Landsat 8/9 OLI for 2023 from USGS EarthExplorer) spanning a 33-year period from 1990 to 2023 at decadal intervals to analyse the Land Use and Land Cover (LULC) dynamics of the Ganga and Punpun Rivers [42]. To ensure spectral consistency and minimize seasonal phenological variations, all images were selected from the pre-monsoon season (typically March to April), characterized by minimal cloud cover and distinct signature differentiation between permanent water bodies, sand bars, settlement/build-up and vegetation area. Image processing and spatial analysis were conducted using ArcGIS 10.8 software using WGS 1984 UTM Zone 45N projection, where a supervised classification approach was implemented. Specifically, the Maximum Likelihood Classification algorithm which calculates the probability of a pixel belonging to a specific class based on the means and covariance of training signatures was employed alongside Minimum Distance criteria to categorize the landscape into distinct classes such as water bodies, agricultural land, built-up areas, and vegetation. Accuracy was checked through confusion matrix assessment. Methodology flow chart of LULC map creation is given in Figure 2.

Statistical Analysis

All the statistical data were analysed using Microsoft Excel, 2019 and OriginPro 2025 (learning edition). ANCOVA and the non-parametric Mann-Whitney U-test were conducted to assess significance among various parameters at the 5% and 1%.

3. Results

A total of 506 individuals were analysed from both rivers (Ganga 257; Punpun 249) having mean length of 8.39±1.33 cm and 8.40±1.28 cm recorded from the river Ganga and Punpun, respectively. Figure 3 illustrates the length–weight distribution, indicating that the predominant length group in the Punpun river ranged from 8.2 to 9.2 cm, whereas in the Ganga river it ranged from 7.5 to 8.0 cm. The weight distribution showed that most individuals from the Punpun river fell within 4–6 g, while those from the Ganga river were primarily in the 5–7 g range. It revealed that the Ganga river population had higher weight, but the Punpun river had more length groups covered. Table 1 depicts the descriptive statistics and estimated regression parameters of LWRs. The length-weight relationship equations for the Ganga and Punpun rivers are derived as W = 0.0129 L^3.023 (r² = 0.944, p<0.05) and W = 0.0079 L^3.169 (r² = 0.962, p<0.01), respectively. The population of P. sophore from both rivers exhibits a positive allometric growth pattern. Table 2 depicts the condition from both the river systems. Fulton’s condition factor (Kc), relative condition factor (Kn) and allometric condition factor (Ka) were recorded as 1.143, 0.997, and 0.799 from the river Ganga, whereas 1.364, 1.001, and 1.299 from the river Punpun, respectively. The Mann–Whitney U-test revealed a significant difference (p<0.05) between the Kc and Ka values of the Ganga and Punpun rivers. In contrast, the Mann–Whitney U-test for Kn values showed no significant difference (p = 0.535; U = 1620). The condition factor for the river Punpun indicates that species within this river thrive under favourable conditions; conversely, the same species in the Ganga river have relative and allometric condition factor values below 1, signifying an unsuitable environment. Table 3 shows the growth parameters of the species from both the river systems. Asymptotic length (L_∞), growth coefficient (K), and age at zero length (t₀), were estimated as 13.46 cm, 0.71 yr⁻¹, and -0.548 years for the river Ganga, and 13.34 cm, 0.735 yr⁻¹, and -0.538 years, respectively for the river Punpun. The Von Bertalanffy growth equation was determined as Lt = 13.46 [1-e^{−0.710(t + 0.548)}] for the Ganga river stock and Lt = 13.34 [1-e^{−0.735(t + 0.538)}] for the Punpun river stock. The growth performance index (φ’), which reflects the overall growth performance of the fish population based on length increment, was estimated at 2.109 for the river Ganga and 2.050 for the river Punpun population. The corresponding maximum lifespan (tmax) was calculated as 3.67 years for the river Ganga and 3.543 years for the river Punpun. The ELEFAN graph of the species from both the rivers are presented in Figure 4 a &b. Total mortality (Z) was estimated to be significantly higher for the river Ganga (1.822 yr⁻¹) compared to the river Punpun (1.797 yr⁻¹). The current fishing mortality (F) values for P. sophore were 0.463 yr⁻¹ in the river Ganga and 0.399 yr⁻¹ in the river Punpun, both below the optimal fishing mortality (1.3 yr⁻¹). Correspondingly, the exploitation rates in both rivers were also lower than the optimal level, indicating under exploitation of the stocks. The M/K ratio for the river Ganga and Punpun was estimated at 1.914 and 1.902, respectively. The typical M/K range is 1.0–2.5; therefore, the both river stock falls within the expected range. These findings indicate that P. sophore populations in both rivers are subjected to under exploitation. Puntius sophore from both the rivers exhibits peak recruitment during the monsoon months (June -August), representing approximately 55-60% of total recruitment (Figure 5). Similarly F/M for the both rivers shows <1, indicating light fishing in both ecosystems.

The VPA reveal that the maximum fishing exploitation occurred within the length range of 6.5–9.5 cm and 7.5–9.5cm in the river Ganga and Punpun, respectively (Figure 6 a & b). It indicated a substantial increase in fishing mortality for individuals larger than 6.0 cm from both the rivers. The Thompson and Bell yield prediction model estimated F_max values of 5.0 for the river Ganga and Punpun. The F₅₀% values were 1.39 and 1.32, both of which were higher than the current fishing mortality, indicating fishing pressure in both riverine systems are under controlled for this species (Figure 7 a & b). The estimated Biological Reference Point (BRP), considered equivalent to natural mortality (M), indicated that fishing mortality below the BRP for both riverine stocks. The BRP values were 1.359 for the Ganga River and 1.398 for the Punpun River, suggesting that both populations are experiencing excessive natural mortality due to predation.

The Length-based SPR values for Ganga and Punpun rivers ranged from 0.32 to 0.40, as illustrated in Table 3. The current situation of gear selectivity indicates that selectivity length of gear (SL), is less than the maturity length (Lm) of the species, as illustrated in Figure 8, where it is readily apparent that SL < Lm for both rivers. However, the selectivity length and SPR values below 40% indicate that species P. sophore experiences recruitment overfishing. The maximum SPR of 0.40 for the Punpun River signifies that with appropriate management practices, the stock in this river can be sustained. Conversely, for Ganga river (0.32), stocks are under depleting condition and could be managed through a complete ban on fishing in the breeding grounds, alongside an increase in mesh size to correspond with its maturity length.

The multi-decadal spatiotemporal analysis from 1990 to 2023 indicates a substantial transformation in the Ganga–Punpun landscape (Figure 9Figure -11) influenced by rapid human development and river dynamics. The most significant difference noticed over the 33-year study period is the exponential expansion of build-up areas (89.11%) (Fig 9 & 11). The confined focusing area, indicated by the black oval on the time-series maps illustrates that urban and infrastructural expansion was significantly limited during the 1990s and early 2000s (Figure 10). However, a prominent acceleration happened post 2010, resulting in an extensive urbanization by 2023 (Figure 10). Simultaneously, the river's morphology endured significant alterations, marked by variable surface water limits and pronounced sedimentation. The active river channels exhibited distinct structural modifications and braiding over time, which directly corresponds to a 30.5% net increase in sandbar formation and diminished by around 18.78% vegetation cover underwent a net reduction of 12.5% (Figure 9). No significant changes were found along the Punpun River concerning settlement and other urbanization activities over the past 33 years (Figure. 10). However, changes in bare land have been seen over the time period along the Punpun River. This specifies a few changes in river ecology and structure, which welfares the natural resources of that river system.

The expansion of these features has directly impacted the region's natural resources. Although vegetation persists as the predominant spatial matrix across time, it demonstrates noticeable fragmentation, especially in regions shifting to barren landscapes or constructed habitats. The significant temporal variability of "bare fields," which transitioned from the south-eastern regions in 1990 to the northern and north-eastern areas by 2023, indicates escalating cycles of agricultural clearance, land fallowing, or preparatory clearing for future urban development (Figure 10Figure -Figure 11). The reliability of these spatial transitions was supported by confusion matrix indices, which yielded classification accuracies ranging from 79% to 86%. The contrasting tendencies between developmental expansion and natural resource deterioration underscore significant environmental stress on the surrounding riparian ecosystem. Collectively, these findings highlight a concerning trajectory of habitat fragmentation and progressive degradation of aquatic ecosystems.

4. Discussion

Length–weight relationship analysis revealed that the estimated exponent (b) value of the species from the rivers Ganga and Punpun was 3.02 and 3.17, respectively, indicating positive allometric growth in both rivers. Similar positive allometric growth of the same species was also reported from the rivers Padma, Sutlej, and Ganga, where the exponent (b) values were 3.12, 3.06, and 3.14, respectively [13,43,44,45]. However, other studies have documented negative allometric growth for this species [46,47], suggesting that growth patterns may vary depending on environmental conditions, habitat characteristics, and population structure [13,17,44]. According to Froese [31], the expected optimum range of the exponent ‘b’ lies between 2.5 and 3.5. In this study recorded b values greater than 2.5 from both rivers, indicating that the estimated parameters fall within the acceptable biological range and reflect normal growth conditions for the species. Condition factor is widely used to assess the overall well-being and physiological status of fish, and it is influenced by a combination of biological and environmental factors including feeding intensity, disease, and physiological condition [30]. In the present study, the estimated Fulton’s, relative, and allometric condition factors for specimens from the river Punpun were >1, whereas the values recorded from the river Ganga were <1, indicating comparatively better growth and favourable environmental conditions for the species in the river Punpun. Barnham and Baxter [48] reported that a well-proportioned fish generally exhibits a condition factor value above one, and variations in this index are closely related to biological interactions, particularly intraspecific competition for food and habitat. These variations are further influenced by factors such as sex, maturity stage, stomach fullness, and food availability. Additionally, variability in condition factor may arise from differences in nutritional preferences and morphological characteristics between juvenile and adult stages of the species [45]. Muchlisin et al. [49] suggested that condition factor values greater than one indicate favourable feeding conditions and/or lower predator pressure, whereas values less than one reflect limited food availability or higher predation pressure. However, condition factor is not species-specific and may vary temporally with season, habitat quality, and environmental conditions.

As, globally, limited information is available on the stock parameters of P. sophore. In the present study, the estimated asymptotic length (L_∞) of the species from the river Ganga, and Punpun, were 134.60 mm, and 133.42 mm, respectively. These values are comparable with earlier observation made from the floodplain water of Bangladesh, where the asymptotic length of the species was 135 mm [50]. However, these findings are not corroborated with other studies made from river Sutlej and old Brahmaputra, where recorded L_∞ was 74.17 mm, and 123.5 mm, respectively [44,51]. Such differences in asymptotic length may be attributed to variations in sample size, size range of specimens collected, environmental conditions, and the type of fishing gears used for capturing the fish stock. According to Branstetter [5], fish species can be classified into three categories based on the growth coefficient (K) such as slow-growing species (0.05–0.10 year⁻¹), moderate- or average-growing species (0.10–0.20 year⁻¹), and fast-growing species (0.20–0.50 year⁻¹). The estimated growth coefficient of the species from river Ganga and Punpun was 0.71 year⁻¹ and 0.74 year⁻¹, respectively. It indicates that the species has fast growth in both river systems. These findings are consistent with earlier studies of Kumar et al. [44], Mustafa and Graaf [50], and Ahamed et al. [51], who reported comparatively higher growth coefficient values, 1.18 year⁻¹, 1.10 year⁻¹, and 0.93 year⁻¹, respectively, indicating rapid growth of the species across different water bodies. The growth performance index (Ø'), a metric for assessing the relative well-being of aquatic organisms within their habitat, is crucial for measuring their overall condition [53]. In the present study, slightly higher growth performance index (Ø′) was observed for the river Ganga compared to the river Punpun, indicating better overall growth performance and general well-being of the population in the river Ganga. Assessing the mortality rate is important for understanding the depletion rate of stocks in natural environments. In the present study estimated total mortality (Z) was ranged from 1.79 to 1.82, which is lower than some earlier reports, where considerably higher values (2.26 to 6.55) have been documented [44,50,51]. Such variation in total mortality is common among short-lived small indigenous species and may be attributed to differences in environmental conditions, fishing pressure, recruitment variability, predation and population structure. Additionally, methodological differences, including sampling period, size range of specimens, and analytical approaches used for mortality estimation, can substantially influence Z values. P. sophore experience optimum fishing pressure (E < 0.5) in both riverine systems, with comparatively greater exploitation in the river Ganga. Healthy fish stocks are generally characterized by an exploitation rate (E) equal to 0.5, whereas overexploited stocks exhibit E values exceeding 0.5 [54]. The ratio of natural mortality to growth coefficient (M/K) is commonly reported to range between 0.5 and 4.0 [55], while Beverton and Holt [56] suggested a narrower range of 1.0–2.5 for most fish species, which is often used to evaluate the reliability of natural mortality estimates. In the present study, the M/K ratio (1.9) fell within the general acceptable range. A stock is considered as mortality dominated when the Z/K ratio is 2 or higher, and as growth dominated when the Z/K ratio equals 1. In the present study, both rivers exhibited mortality dominance, as the Z/K ratio exceeded 2. It indicates that mortality plays a greater role than growth in regulating the population, suggesting that the fish stocks in both rivers are subjected to under optimum exploitation with higher natural mortality. P. sophore from both rivers exhibited peak recruitment during the monsoon months (June–August), which is consistent with earlier reports based on gonadosomatic index (GSI) and histological observations. Choudhury et al. [57] reported peak recruitment in July, whereas Hasan et al. [1] documented an extended recruitment period from April to June, indicating that the major recruitment phase of the species generally coincides with the monsoon season.

In fisheries management, biological reference points are used to guide sustainable exploitation. Two important reference points derived from yield-per-recruit (YPR) analysis are F₀.₁, the fishing mortality corresponding to a 10% increase in yield per recruit, and Fmax, the fishing mortality that produces the maximum yield per recruit [5]. In the present study, YPR-based reference points were estimated for both river stocks. The observed fishing mortality (F) values were lower than Fmax and F₀.₁, indicating that the P. sophore populations in both rivers are under optimum condition. However, fishing pressure in Ganga river is higher compare to Punpun river. Gulland [41] suggested that, for an optimally exploited stock, fishing mortality should be approximately equal to natural mortality (Fopt ≈ M). In the present study, the estimated F values below this biological reference point, further indicating that the stocks in both rivers are optimum. Owing to significant overfishing and the declining ecological integrity of several river systems, the P. sophore stock necessitates replenishment via diminished fishing operations to attain the Maximum Sustainable Yield.

The spawning potential ratio (SPR) is an established biological reference point and an efficient method for evaluating the effects of present fishing pressure on the stock's reproductive capacity [40]. According to Hordyk et al. [40], an SPR index value below 0.2 indicates stock depletion, value between 0.2 and 0.3 indicates an overfishing stage, value between 0.3-0.4 fishing with sustainable approach and above 0.4 indicates the stock is in a healthy state. Additionally, in the context of national fisheries laws, an SPR value of 0.40 is commonly regarded as a stand-in for Maximum Sustainable Yield (MSY). Overall, the results show that the LB-SPR rate for P. sophore from the Gnaga River is 0.32 and from the Punpun River is 0.40. According to the current study, the P. sophore population in the Punpun river is in a sustainable fishing state but Ganga river stock shows near to over fishing stage. Further F/M values (<1) also suggest the under-exploitation status of P. sophore in both observed rivers; however, future exploitation must be maintained at a sustainable level to ensure the resource can be utilized over an extended period. After analyzing the stock status of Daysciaena albida, Eleutheronema tetradactylum, and Mugil cephalus in the Chilika lagoon in India, Suresh et al. [59] came to the conclusion that the selection of a data-limited methodology should be based on the features of the available data and local fishery conditions, with the Catch Maximum Sustainable Yield (CMSY) being useful for catch data and LB-SPR for length-frequency data. By increasing mesh size and reducing fishing effort by 50%, it is possible to optimize the stock depletion situation. This will ensure that fish greater than 7.5 cm are collected while preventing the capture of smaller species. Fish population studies and stock assessments are two of the many uses of virtual population analysis that are significant and have been used as tools for fisheries management worldwide. Because P. sophore is a significant food fish, a dataset for its stock assessment and sustainable use must be created.

Hasan et al. [60] assessed the land use and land cover (LULC) transition of the entire Ganga basin over nearly three decades (1992-2020) and reported that the settlement area was increased from 15.57 to 83.55%. Conversely, green areas diminished by 15.47%, while water areas expanded by 2.69% over the past three decades. In the present study, the settlement area increased by 89.11%, whereas the green zone and water area decreased by 12.55% and 18.78%, respectively. The condition of fisheries in the Ganga and Punpun river basin is extremely concerning, as the research indicates a "squeeze" effect in which increasing urban development is physically and chemically suffocating aquatic habitats. The loss of greenbelts at the same time makes the environment worse by removing natural filters, which causes more siltation and higher water temperatures. The degradation of nursery habitats, diminished regions, and increased urban pollution pose significant threats to native fish populations, jeopardizing the livelihoods of millions reliant on the basin's aquatic biodiversity. Such pronounced environmental changes necessitate the immediate implementation of effective management strategies to mitigate biodiversity loss and conserve riverine fish stocks. In a context identical to our study, Castello et al. [61] demonstrated that alterations in land use and land cover (LULC) patterns ecosystems can affect fish populations and catch rates; their research focused on two medium-sized rivers in Cameroon, South Africa. Similarly, Alam et al. [34] clarified that changes in LULC surrounding wetlands have resulted in changes to fish diversity patterns over a six-year period. Population growth and urbanization are the primary causes of deforestation adjacent to aquatic ecosystems. These long-term alterations exceed sustainable limits, adversely impacting inland fisheries due to modifications in land use and land cover, resulting in diminished riverine and wetland ecosystems. The study concludes that regulatory measures are essential to alleviate fishing pressure and achieve optimal exploitation levels sustainably of the species in Indian rivers. Fishing must to be prohibited during the mating season, and mesh size limitations must be rigorously enforced to enhance the sustainability of self-recruiting wild fish populations. Moreover, altering the environment via conservation site designation and fishing restrictions could substantially influence the sustainability and stock structure dynamics of P. sophore in riverine ecosystems.

5. Conclusions

This study established baseline data showing that Puntius sophore populations in the Ganga and Punpun rivers exhibit significant positive allometric growth with stable, under-exploited stock dynamics; yet, they are facing considerable and diverse anthropogenic pressures. The Punpun River provides a more favourable feeding and physiological environment, as well as sustainable harvesting with a stable SPR. Conversely, the Ganga River stock exhibits significant fishing pressure with a low SPR, indicative of conditions approaching overfishing. It faces high threats from a multi-decadal landscape transformation (1990–2023), characterized by an 89.11% increase in built-up areas, an 18.78% decrease in water bodies, and intensified siltation. The fast urbanization and habitat fragmentation create a significant "squeeze" effect that physically impacts aquatic habitats, which may increases natural mortality due to predation and disturb the feeding and breeding grounds. These challenges have enduring consequences for millions of individuals who directly or indirectly depend on fish for protein, resulting from loss in fish production and biodiversity from Indian rivers. Urgent regional management strategies are necessary to conserve native fish populations and ensure long-term sustainability. The measures must consist of a comprehensive fishing restriction during the peak monsoon breeding time, strict adherence of proper gear mesh sizes to minimize exploitation of immature fish, and the formation of protected riparian conservation zones to mitigate severe habitat degradation.