Stock Evaluation of the Euryhaline Cichlid, <i>Etroplus suratensis</i> (Bloch, 1790), from Significant Brackish and Freshwater Regions of India

Haque, Ramjanul; Sawant, Paramita Banerjee; Sundaray, Jitendra Kumar; Kumar, Rajesh; Chadha, Narinder Kumar; Biswas, Gouranga; Rasal, Avinash; Nandanpawar, Priyanka; Debbarma, Jackson

doi:https://doi.org/10.1155/2024/7208918

Journal of Applied Ichthyology

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Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 7208918 | https://doi.org/10.1155/2024/7208918

Stock Evaluation of the Euryhaline Cichlid, Etroplus suratensis (Bloch, 1790), from Significant Brackish and Freshwater Regions of India

Ramjanul Haque,¹Paramita Banerjee Sawant,¹Jitendra Kumar Sundaray,²Rajesh Kumar,²Narinder Kumar Chadha,¹Gouranga Biswas,¹Avinash Rasal,²Priyanka Nandanpawar,²and Jackson Debbarma²

Academic Editor: Jorge Paramo

Received12 Sept 2023

Revised04 Jan 2024

Accepted23 Jan 2024

Published09 Feb 2024

Abstract

The present study evaluates stocks of Etroplus suratensis concerning length-weight relationships (LWRs), condition factor (K), and truss analysis from different habitats, viz., West Bengal (freshwater hatchery), Gujarat (brackish water grow-out pond), and Odisha (Chilika lagoon) in India. All the stocks were collected using gill nets with various mesh sizes of 20–50 mm and cast nets with mesh size of 15 mm from February 2021 to September 2021. The total length (L), weight (W), and digital pictures were taken for LWRs and principle component analysis (PCA). The total length (L) and total weight (W) were measured to the nearest 0.1 cm and 0.01 gm, respectively, followed by regression analysis. The values of parameters “b” in the LWRs equation were estimated within the range of 2.50–3.82 and intercept “a” values (0.235–0.779) and “K” (1.07–3.37), respectively, for all different habitats and ecosystems with significant correlation values ( ≥ 0.85). In truss morphometric analysis, principal component 1 (PC1) contributed the highest (93.1%) and principal component 2 (PC2) contributed 4.6% to the size and shape variation amongst the stocks from different habitats and ecosystems. The scatter plot analysis and canonical variate analysis (CVA) have shown that the Gujarat and West Bengal stocks are closely placed and separated from the Chilika-Odisha stock. Similar variations were also observed in the ANOSIM boxplot analysis, where Chilika-Odisha stock scored higher than the other two stocks. Since data available regarding LWRs and truss morphometry studies of E. suratensis from different habitats are limited, the present study will provide a clear insight into the differentiation of E. suratensis stocks from different habitats in India. The overall findings of the present study could be utilized for the conservation and sustainable management of E. suratensis.

1. Introduction

The pearl spot, Etroplus suratensis, is an euryhaline cichlid that is distributed mainly in brackish water along with freshwater ecosystems in India and Sri Lanka [1]. In India, the wild populations have been recorded primarily from the states of Kerala and Tamil Nadu, and some introduced populations were also reported in Goa, Andhra Pradesh, Odisha, and West Bengal [2, 3]. This species has been introduced to many states along India’s east, south, and west coast to promote sustainable aquaculture [4]. Understanding population diversity between freshwater and brackish water, wild stock, and hatchery-reared stock from east, south, and west coast of India is essential for stock assessment, its management, and the conservation of an endemic population like E. suratensis [5]. Despite this fish’s economic and evolutionary importance, there is a dearth of comparative studies of its population or stock characteristics from significant habitats in India, with reference to LWRs, “K”, truss network system, and PCA-based stock characterization.

Every animal exhibits growth in length and weight during its lifetime, and the relationship between the two has practical and fundamental significance. In fishery assessments, the length-weight relationship is one of the fundamental techniques that produce reliable biological data [6]. Growth parameters used to indicate demographic differences, biological characteristics, and habitat conditions of fish species include the LWR and “K” [7]. The “K” value gives information on the physiological state of the fish and its well-being. The LWRs can also be used to determine possible differences between distinct stocks of the same species and provide information about the stock condition [8].

The truss network system is an alternative to traditional measurements and is highly impressive in gathering information on size and shape changes [9] among different stocks. It involves systematic measurement of distances (i.e., truss lines) between pairs of landmarks across the body, thus forming a sequential series of connected polygons termed a “trussed” box [10], which covers the entire shape of the fish in the form of a uniform network. Thus, the probability of delineating the morphometric differences among the different fish stocks is increased [11]. Despite the ecological importance of freshwater and brackish water fishes, information on length-weight relationships and condition factors is often limited. Even though there has been some work on truss analysis of riverine and lake fish populations [12, 13], there is no report on truss analysis of E. suratensis from varying habitats or ecological regions to date. E. suratensis has been reported in the IUCN red list [5] due to anthropogenic interventions such as pollution of water bodies, over-exploitation, and natural habitat destruction [14]. Hence, it is imperative to study the growth variation in population from different habitats and ecosystems of this species from various water bodies, either in the wild or under domestication throughout India. Therefore, the objective of the present study was to evaluate the population characteristics of E. suratensis to identify any morphological distinctions that could be useful in differentiating possible stocks occurring in significant habitats and coastal regions of India.

2. Materials and Methods

2.1. Stock Collection

This study was carried out in three different parts of India, such as hatcheries from West Bengal, the grow-out pond of Gujarat, and the Chilika lagoon. Hatcheries near the brackish water region in West Bengal are well established, and the cultural practices depend on formulated feed. The brackish water pond in Gujarat (Navsari region) is 5 km west of Dandi beach, adjacent to the Purna river estuary, bringing in nutrient-rich brackish water for aquaculture. This entire culture is also being practiced by applying formulated feed and natural food. Hydrologically, the Chilika lagoon is influenced by three subsystems, including the Mahanadi river system, rivers flowing in the lagoon from the western catchment, and the Bay of Bengal. This Chilika lagoon receives freshwater from a series of 52 channels in Odisha. Sampling was performed periodically every month from February 2021 to September 2021 at respective sampling stations. The coordinates of the sampling stations are given in Table 1. A total of 440 numbers of E. suratensis were collected using gill nets with various mesh sizes (20–50 mm) and cast nets with a 15 mm mesh size [15, 16], and their identity was confirmed with the study of Gunawickrama [17]. The current ecological scenario of E. suratensis is depicted in Figure 1.

2.2. Length-Weight Relationship

Total length (L) was measured with a digital caliper (with ±0.1 mm precision) and weighed with an electronic scale to the nearest ± 0.01 g. The length-weight relationship was calculated by the method of least squares using the equation of W = aL^b with 95% confidence limits of “a” and “b” along with the coefficient of determination () determined and logarithmically transformed (Log base 10) into LogW = Loga + bLogL [18, 19], where W is the total body weight (g), L is the total length (cm), b is the regression coefficient, and a is the intercept of the regression. LWRs were estimated for both sexes together.

2.3. Condition Factor

The relative condition factor (Krel) of E. suratensis was determined according to the following equation of Krel = W/(aL^b) [6, 20], where, W is the observed total body weight () and aL^b is the estimated weight from the length-weight relationships. Outliers were detected and removed before fitting linear regression [6]. A good growth state of the fish was identified when the Krel value ≥ 1.0, while a fish in poor growth conditions, the Krel value < 1.0 [20, 21]. A one-sample t-test was used to verify significant differences between the Krel and the expected value of Krel = 1.0 [22].

2.4. Truss Analysis

For digitizing images of fishes, a cyber-shot DSC-W300 digital camera was used. The files were converted from jpeg format to tps format by using tpsUtil [23]. The extraction of truss distances from the digital images of specimens was done with the help of tpsDig2 V2.1 [23] and analyzed using Paleontological Statistics (PAST) software [24]. A total 11-point truss network [25] was constructed using standard morphological landmarks in digitized images, and all landmarks are represented in Table 2 and Figure 2. Truss measurements were taken on 93 specimens of E. suratensis fish from three different habitats. Data regarding length, weight, and trusses were recorded for different stocks of E. suratensis. During analysis, the outliers were removed.

2.5. Statistical Analysis

Before proceeding with PCA, the data were analyzed for normal distribution and significant differences using one-way ANOVA along with Levene’s and Welch’s F tests. The data obtained from truss analysis were subjected to data reduction, and interpretation was completed using PCA and CVA in PAST software. Further, the data were analyzed for similarities using ANOSIM. This test is a generalization of the univariate ANOVA and has the property to consider all variables while calculating similarity among populations based on the Euclidean distance matrix. Statistical analyses, including regression and correlation estimation, were done with Microsoft Office Excel (2013), SPSS ver.22.0, and PAST ver.3.26 software.

3. Results

3.1. Length-Weight Relationship

In the present study, 440 specimens of E. suratensis belonging to Cichlidae family from three different habitats and coastal areas of India are considered. LWRs of the specimen studied were highly significant, with the coefficient of determination () ranging from 0.894 to 0.974 among three different stocks (0.829–0.953) among male and female stocks of three respective habitats. The Chilika-Odisha stock showed positive allometric growth (b = 3.82), whereas the Gujarat and West Bengal stock showed negative allometric growth with b = 2.57 and b = 2.60, respectively. The values for sample size (n), total length (in cm), and body weight (in gm) range and LWRs equations with 95% confidence limit of “a” and “b” are represented in Tables 3 and 4. LogL-LogW relationship among the stocks from three different habitats (in Figures 3(a)–3(c)) and their combined relationship (in Figure 3(d)) are highly correlated. A boxplot summarizing the length distribution of E. suratensis by stock and sex is shown in Figure 4. In Figure 4, it was mentioned that the length-weight relationship of E. suratensis by stock and sex wise is highly correlated for Chilika (CH) and West Bengal (WB) regions and comparatively lesser in Gujarat (GJ) region.

(a)

(b)

(c)

(d)

3.2. Condition Factor

The mean estimated condition factor of the stocks from different habitats with a 95% confidence interval for all 440 E. suratensis species is given in Table 5. All values were higher than “1”, indicating that all stocks were in good condition. The result of Kruskal–Wallis for test independent samples indicated no significant differences in the distribution of condition factors across all of the stocks (). The effect of sex ratio on condition factor of stocks from different habitats stock was also not significant in the present study ().

3.3. Truss Morphometric Analysis

The univariate statistics (ANOVA) of truss measurements showed significant differences between all three groups () with variable F-stat values. Multivariate analyses using Pillai’s trace, Wilks’ lambda, and Hotellings’s trace showed a significant contribution of all groups to the total variation with . The mean ± SE for all landmark distances is given in Table 6. To assess the size and shape variation in all three stocks of E. suratensis, PCA was performed on log-transformed measurements of 11 landmarks with Jolliffe cut-off of 0.01. The eigenvectors and eigenvalues obtained from the covariance matrix allowed the representation of the largest part of the variance of original variables in the first two components, viz., PC1 and PC2. It was observed that PC1 contributed the highest (93.1%), followed by PC2 (4.6%) to the size and shape variation, respectively, amongst the three stocks (Table 7). Eigenvalues for PC1 was 1.28 with 83% variance, while for PC2, it was 0.3, explaining 17.9% variance (Table 8).

Contribution by the third principal component (PC3) was not significant (0.8%). The scatter plot showed that the West Bengal and Gujarat stocks were closely spaced, while the Chilika-Odisha stock was grouped separately (Figure 5). Highest separation was observed between Gujarat and Chilika (R = 0.84), followed by West Bengal (W.B.) and Chilika (R = 0.66). The quantum of separation between Gujarat and West Bengal was low (R = 0.27), possibly because the former were grow-out and Chilika stock was collected from wild (Table 9). The analysis of similarity by Bray–Curtis distance measure ANOSIM method showed significant separation of all groups with value <0.001 (presented in Table 9). The ANOSIM boxplot for different stocks showed highest variation and ranked distances in Chilika stock (Figure 6). The results of the scree plot were in agreement with the results of PCA plot is presented in Figure 7. When the correlation matrix based on PCA was analyzed, it was found that PC1 contributed the highest (71.1%), followed by PC2 (15%), with significant loadings on PC2. The truss distances with meaningful loadings on PC1 were (1–4), (1–5), (1–6), (1–7), (1–8), and (1–9), while for PC2, it was (7–11), (6–11), (5–11), (8–11), (2–7), and (2–9) and significant landmarks are being represented in Table 2 and Figure 2.

Fish stock evaluation often involves the analysis of multiple variables, such as environmental factors, biological characteristics, and habitat conditions. The PCA identifies the dominant patterns or trends in the data by capturing the maximum variance in the first few principal components. These components often represent the most significant sources of variation in the dataset, revealing underlying patterns that may be critical for understanding fish stock dynamics. The results of PCA were reconfirmed with canonical variate analysis (CVA), and it was observed that CVA indicated three separate clusters of each population (Figure 8). However, the Gujarat and West Bengal populations were clustered closely but separately as against to PCA, where they are closely placed and overlapped as compared to Chilika indicating their morphological proximity.

4. Discussion

4.1. Length-Weight Relationship

In fishery assessments, the length-weight relationship is one of the established techniques that produce reliable biological data. It establishes the mathematical relationship between the two variables, length and weight. It aids in determining deviations from the expected weight for known length groups, estimating biomass, evaluating fish stocks, researching growth rates, comprehending the life cycle of fisheries, and identifying changes in allometric growth in fish [6, 26–28]. Every fish species will have a unique length-weight relationship for a particular length and weight range. In addition, it may vary between sexes, stocks, or those from other geographical regions. The parameter “a” is a scaling factor for the fish species’ weight at a specific length. The shape parameter “b” refers to the fish species’ particular body type. In 1968, von Bertalanffy created a growth equation for length and its counterpart in weight [6]. In other words, a cubic relationship exists between a fish’s weight and length. For an ideal fish that maintains the same shape, “b = 3” and values of “b,” which is smaller, equal, and larger than 3, indicate isometry, negative allometry, and positive allometry, respectively. In addition, it has been discovered that even if “b” may vary for fish from various environments, different sexes, or for larval, juvenile, and mature fish, it is frequently consistent for comparable fish. The aim of our present study was to evaluate the stock assessment of E. suratensis from two different ecosystems, i.e., freshwater and brackish water region of India. The previous studies were reported about the stock of this species in coastal region mainly. Although the stock evaluation study of E. suratensis in freshwater and brackish water regions is important for several reasons, as it helps to assess and manage the fish populations effectively like understanding the status and health of fish stocks in freshwater and brackish water ecosystems is crucial for biodiversity conservation. Therefore, freshwater and brackish water ecosystems are sensitive to climate change and studying about these fish stocks allows researchers to monitor the impact of climate change on aquatic environments and identify strategies to mitigate its effects on fish populations. In the present study, the values of the parameter “b” were found within the range of 2.5–3.8, which is within limits for most fish species [6]. The values of the intercept and slope “a” and “b” have indicated stock characteristics for the first time in this species from three different regions of India (freshwater and brackish water regions). The present study provides a first-hand and new LWRs data for E. suratensis from different habitats, i.e., freshwater, brackish water, wild and hatchery-reared stock in India. The higher “b” values of the regression slope indicate that the LWRs of a particular species follow the cube law. High “b” values are a reflection of the general condition of appetite and gonad content of the fish [29], wherein fish weight increased in proportion to the utilization of food items that are available for growth and energy [30, 31]. All LWRs are depicted in Tables 3 and 4. Hydrodynamic circulation is a primary factor for most of the lagoon environments’ physical and ecological processes. Freshwater run-off from the drainage basin, combined with saline water inflows from the ocean, results in a wide range of fresh, brackish, and saline water environments within the lagoon, and this spatially and temporally diverse water environment supports an exceptionally productive ecosystem. This might be the reason for higher “b” values were found for the Chilika lagoon site compared to the other two sites. The goodness of fit of the regression model is indicated by the “R²” value in the output. It should be high for the model fitted to be good. In the present study, the estimated coefficients of determination () were within the range of (0.85–1.0), which shows better predictive power and smaller dispersion of LWRs data [32, 33]. Generally, variations in LWR parameters of each fish species occur mainly due to abiotic factors such as seasons, habitats, and biological parameters like size range, sex, maturity stages, and feeding habits [34–36]. Froese [37] provided estimated values of LWR parameters for all known fish species available at FishBase using Bayesian analysis. However, some biological traits of Etroplus suartensis have already been reported at FishBase. No such data are available on the variation in LWR, condition factor, and morphology of the population of this fish from different habitats. This study provides baseline biological information about euryhaline species from different habitats and ecosystems in India.

4.2. Condition Factor

The “K” and seasonal variations in the condition factor, which are helpful in identifying the biological changes in the fish, can also be found using the length-weight relationship. The “K” can be used to determine whether a particular body of water is suitable for fish growth and to distinguish between ecological variables, such as seasonal fluctuations, and the type of aquatic system present, such as rivers or lakes [38]. The level of sexual maturation, the degree of food source availability, the age, and the sex of some species are all indicated by different values in a fish’s condition. Cren [20] also compared the length-weight relationship for perch, Perca fluviatilis, at various life stages, sexes, gonad development phases, and seasons in Lake Windermere. According to Mozsár et al. [39], if the Krel value is less than 1.00, the fish is in poor, lengthy, and thin condition. A decent fish with proper proportions would have a Krel value of roughly 1.40. In the present study, fishes from all the stock showed good condition with “Krel” values above (>1). This can be correlated to the abundance of food and good water quality in the brackish water habitat, i.e., Chilika Lake, a coastal pond of West Bengal and Gujarat, which had a positive effect on the fish condition. Ighwela et al. [40] found that Nile tilapia raised in aquaculture ponds in the USA had values greater than 1. Uganda’s Lake Nyamusingiri and Lake Kyasanduka have values between 1.5 and 1.8, according to Bwanika et al. [41]. When fish usually grow, the condition factor of Oreochromis niloticus is around “1”, rising as sexual maturation approaches [42, 43]. The exponent or slope “b” is essential in determining fish health because the condition factor value has a high correlation to LWR.

4.3. Truss Analysis

Fishes exhibit the highest variance in morphometric traits within and between populations compared to animals. At the same time, they are also vulnerable to morphological variation induced by natural forces [44–46]. Distinctness of morphometric traits can be correlated positively to the divergence and topographical segregation between populations. When geographic barriers separate populations, they evolve different phenotypic characters or features which are often shaped by environment, genetic inflow, and epigenetic factors [47]. These lead to forming or evaluating separate strains, races, populations, and stocks.

In fisheries biology, morphological traits are frequently utilized to assess relationships and discreteness among diverse taxonomic groupings. Numerous well-documented morphometric investigations support stock discreteness. For example, the morphological variation of European cyprinid, the chub, and Leuciscus cephalus, within and across Central European drainages was successfully differentiated by morphometries [48]. The identification of stock becomes a crucial component in any management regime. The study of population factors, as well as physiological, behavioral, morphometric, meristic, calcareous, biochemical, and cytogenetic traits, is helpful for the practical identification of these potential stocks. The morphometric investigations are the most important for taxonomic and management considerations [49, 50]. They are based on a series of measures of the body form.

In the present study, the Jolliffe cut-off value is 0.01 [51], giving an informal indication of how many factors or principal components should be considered as significant. In our case, only two components were found to be contributing maximally to the morphological variation due to their highest eigenvalues. Eigenvalues expected under the Broken Stick Model Curve represent nonsignificant components (scree plot, Figure 7). The PCA analysis indicated morphological similarity between Gujarat and West Bengal even though the geographical distance between them is more. On the other hand, the Chilika-Odisha stock is grouped separately. The reason for this grouping might be the closed native population of Etroplus in Chilika lagoon, where the possibility of population admixture is less. West Bengal and Gujarat stocks are from different habitats, i.e., freshwater and brackish water, respectively. It is speculated that there is a mixing of population from West Bengal and Gujarat despite the distances between them, which can be attributed to several factors such as seed ranching in rivers and stocking from common seed markets. Results from ANOSIM analysis also indicated a similar trend of stock differentiation. An R value close to 1 indicates high separation between stocks, while a lesser value indicates more resemblance between stocks. In this study, Gujarat and Chilika stocks had the highest R (0.83) value, indicating significant separation. In contrast, W.B. and Gujarat stocks had the least R value (0.27), showing little separation amongst stocks with value <0.01.

A careful observation of the PCA component 1 loadings suggests that significant loadings were present on the anterior portion of the fish, as described by Silva [52]. In contrast to this, the second component, which is termed caudal, represented significant loadings on the posterior portion of the fish body. Hence, it can be concluded that the size variation was evident for the anterior portion while shape variation was primarily found near the posterior portion. However, the CVA factor 1^st and 2^nd loadings indicated size and shape variation in the posterior region of the fish body.

5. Conclusion

The present study provided insight into the stocks of E. suratensis from three different habitats and ecosystems in India. It also provides a comparative analysis between the stocks from hatchery and grow-out pond-reared conditions with wild ones. Most fish exhibit allometric growth patterns; thus, long-term studies on fish biology shall be beneficial for a better understanding of factors that influence the growth and physiology of fish. The present study has preliminarily indicated the stock characteristics of E. suratensis from three different ecological habitats for the first time.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Ramjanul Haque performed data curation, investigated the study, provided software, and wrote the original draft. Paramita Banerjee Sawant conceptualized the study, supervised the study, and reviewed and edited the manuscript. Jitendra Kumar Sundaray and Rajesh Kumar supervised the study, proposed the methodology, and reviewed and edited the manuscript. Narinder Kumar Chadha supervised the study, provided software, and performed data curation. Gouranga Biswas edited the article and performed formal analysis. Avinash Rasal provided software, performed data curation, and edited the article. Priyanka Nandanpawar provided software, performed data curation, and edited the article. Jackson Debbarma edited the article and performed formal analysis.

Acknowledgments

The authors would like to acknowledge the director and vice-chancellor of Indian Council of Agricultural Research-Central Institute of Fisheries Education, Mumbai, India, and the director of Indian Council of Agricultural Research-Central Institute of Freshwater Aquaculture, Bhubaneswar, India, for their support and encouragement.

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Copyright © 2024 Ramjanul Haque et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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