Growth, mortality and stock status of five fin fish species from a tropical estuary Snigdhodeb Dutta Department of Industrial Fisheries, CUSAT, Kerala –682022

doi:10.21203/rs.3.rs-1525532/v1

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Growth, mortality and stock status of five fin fish species from a tropical estuary Snigdhodeb Dutta Department of Industrial Fisheries, CUSAT, Kerala –682022

https://doi.org/10.21203/rs.3.rs-1525532/v1

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Data from Cochin Estuary for five fin fish species were collected from January to December 2019. This study provides the first results on fishery dynamics of the species in this estuary and is aimed at estimating important population dynamic parameters and assessing the stock of this economically important fish species by the help of length frequency data collected. FiSAT II software was used for different analyses. The asymptotic length (L∞) and growth constant (K) of C.chanos were 421.05 mm and 0.71 year^− 1, whereas for D.insidiator the asymptotic length (L∞) and growth constant (K) were 110.25 mm and 0.81 year^− 1. The age at zero length (t0) was calculated for all examined species, ranged from − 0.07 to -0.19. The longevity (tmax) and growth performance index (ϕ) of P.planiceps were reported as 7.31 years and 4.81 whereas for M.cephalus were 2.14 and 4.6 respectively. The estimated total mortality (Z), natural mortality (M) and fishing mortality (F) of C.praesteus were 4.04, 1.37 and 2.67 year − 1, respectively, contrarily, for P.planiceps those values were 0.89, 0.49 and 0.4. The relative yield per recruit (Y/R) analysis revealed that the current exploration level is higher than the optimum exploitation rate, indicating an overexploitation of all examined fin fish species. Accordingly, needed management measures should include restrictions on fishing gear mesh sizes, reduced fishing effort and declaration of closed seasons and sanctuaries to protect these species from extinction.

Cochin estuary

Condition factor

Exploitation rate

Length frequency data

Population dynamic parameters

Fisheries research aims to provide forecasts of how a stock and its fishers would react to different levels of fishing effort (King, 2007). Formulating management plans necessitates the employment of mathematical dynamic models. Researchers must first figure out how fish populations grow and perish (Dadzie et al., 2007).Nonetheless, the introduction of length-based stock assessment approaches has made studying the population dynamics of fish stocks in many data-poor fisheries potentially easier (Pauly and Morgan, 1987). Freshwater overfishing, like its marine equivalents, is regarded to be a key contributor in the recent decline of many fish populations (Helfman, 2007). When natural mortality (such as sickness and poor environmental circumstances like pollution and eutrophication) and fishing mortality (overfishing, management designations) outweigh the benefits from spawning, recruitment, and individual growth, fish stocks drop in abundance (Tesfaye and Wolff, 2015)

A growth study is used to determine how many fish can be generated in given water body. Errors in age estimates can contribute significantly to over- or under-exploitation of a species' stock. Direct methods for determining age and growth are possibly the most intricate and contentious topic in fish biology (Nedreaas, 1990). Fish growth is an important feature of their biology and life cycle, and measuring it is a common part of fisheries research and management (Weatherly &Gill, 1987). The balance between life cycle features of reproduction, growth, and mortality determines the structure of a population (Sissenwine, 1984).

Certain fisheries evaluation methodologies necessitate the division of overall mortality into components attributable to fishing and natural causes. Pauly (1980) developed a multiple regression model for predicting natural mortality using growth characteristics and mean environmental temperature that has been widely used to estimate natural mortality in fish stock assessments. Direct readings of hard structures (e.g., otoliths, spines, and vertebrae) to estimate age, or indirect guesses based on length distribution data across time, are two traditional ways for assessing growth characteristics of fish populations (Gayanilo et al.,2002). In tropical and subtropical contexts, indirect stock assessment approaches are more effective since hard features in these moderately warm waters, when seasonal changes are small, can occasionally exhibit false band patterns. In recent decades, these methods have been utilised to evaluate life-history hypotheses and give extent consistent of biological and fisheries features such as initial maturity duration and longevity (Sparre & Venema, 1992). As a result, stock evaluation becomes a fundamental management tool for understanding fish development (both individual and population) and death (Jennings et al.2001). It also aids in the forecast of fish population exploitation, which could aid in the selection of different management options (Froese, 2006). There is a widespread absence of growth and mortality estimates for coastal marine fish populations. These are crucial factors for predicting the dynamics of fish stocks and ecosystem populations (Sparre & venema, 1992).

For evaluating age and growth in freshwater fishes, the length-frequency approach is widely acknowledged, and it remains the best method for determining growth parameters for tropical and data-poor fish stock assessments. The inverse Von Bertalanffy Growth Function (VBGF) may be used to convert length into age and weight using length and weight-derived growth factors.Due to the difficulty of using direct ageing methods on species, scientists have developed a number of microcomputer programme packages for age and growth assessment, including LFSA (Sparre, 1987), COMPLEAT ELEFAN (Gayanilo Jr et al., 1988), MULTIFAN (Fournier et al., 1990), LFDA (Holden and Bravington, 1991), and FiSAT (Holden and Bravington, 1991).We used FiSAT II (Gayanilo et al., 2005) and length frequency data of fish specimens fromCochin estuary estuary to estimate growth parameters.The use of a single technique to determine age and development has its own set of restrictions, especially when using indirect/statistical methods. This is the first complete investigation of these species' population dynamics from this estuary. The goal of this study was to establish population dynamic factors for five fin fish species caught from Cochin estuary using length frequency data acquired from January to December 2019. With the objective of establishing efficient management strategies for this fish species, we assessed characteristics such as mortalities (Z, F, M), growth rates, lifespan, and exploitation rate.

2.1 | Study area

The largest estuary system along India's west coast is the Cochin Estuary (9400–10120 N and 76100–76300 E). The Cochin Estuary is 450 m–4 km wide and 3–15 m deep, and it is part of the Cochin estuary–Kol wetland system (Shivaprasad et al., 2013). A salinity barrier has separated southern freshwater and northern estuarine ecosystems at Thaneermukkam. The estuary's fisheries exploitation has been reported to be on the decline (Asha et al., 2016). The absence of numerous major marine migrants has been connected to a decrease in total landings (Maitra et al., 2018).

2.2 | Sampling and data analysis
Length frequency data were collected from 3 distributed sampling sites. Fish were caught between January to December, 2019 by various gears like ring seine (mesh size of 8–26 mm), smaller mesh sized drift gill net (26–90 mm) , cast nets with a ( 15-20mm). Fish were gathered monthly from Munambam Fisheries Harbour (Lat. 10°10′965″N, Long. 76°10′258″E), Cochin
Fisheries Harbour (Lat. 09°56′327″N, Long. 76°15′764″E) and Chellanam (Lat. 09°47′950″N, Long. 76°16′551″E). The monthly sampling was done during morning hours (from 06:00–10:30) and the randomly captured samples were selected. The specimens were sorted and identification was based on Anderson and Heemstra (2003). The assessment of the fish in the field was the body weight to a precision of 0.1 g by utilizing an electronic balance and the total length to the imminent of 1 mm with the aid of vernier calipers.

2.3|Length Classification

During our study, a total of 2698 fish specimens of 5 species (Planiliza planiceps (540),Mugil cephalus (584), Chanos chanos (432), Carangoides praeustus (522) & Deveximentum insidiator (620)) were measured for the length frequency and the length-frequency data monthly basis were pooled for 1 year.

2.4| Estimation of the Length-Weight Relationship and Condition Factor (K)

The Weight-length relationship was calculated using Eq. 1, where a and b are parameters, TL is the total length (mm), and W is the weight (g) (Froese, 2006):
W=aTL^b (1)

Logarithmic form of the weight-length relationship, with variables and parameters defined as above (Eq. 2) (Beverton and Holt, 1957).

LogW = Log a+b Log TL (2)

The coefficient of determination (r2) was utilised to assess the linear regression's quality. The value of b indicates the type of growth of the fish. The condition factor is used to compare fish condition, fatness, or wellbeing (Mir et al., 2012), with the assumption that heavier fish of a given length are in better condition. Extreme outliers were excluded from the regression computations, according to Froese (2006). The difference in condition factor has been regarded as a measure of histological events such fat reserve, environmental adaptation, and gonadal development (Le Cren, 1951).The coefficient of the condition, K was calculated using Fulton (1904):
K = 100*W/L³ (3)

Where W is the weight (g), L is the length (cm) and 100 is a factor to bring the value of K near unity.

Modified condition factor is also being calculated using (Ricker, 1975):

K=100*W/L^b (4)

Where W is the weight (g), L is the length (cm), b is exponential form of slope and 100 is a factor to bring the value of K near unity.

Relative condition factor is being calculated using(Le Cern, 1951)

K =W/ al^b(5)

Where W is the weight (g), L is the length (cm),b is exponential form of slope and a is exponential form of intercept.

2.5|Estimation of Growth Parameters
The Von Bertalanffy Growth Function (VBGF) needs the use of three parameters to explain fish growth. The asymptotic length, abbreviated L, is the potential maximum length that a fish population member can achieve. K is the rate at which length approaches maximal size. The theoretical age at which fish specimens would have reached zero length if they had grown according to VBGF is provided as. The ELEFAN I (Electronic Length Frequency Analysis) module of FiSAT II software was used to calculate the asymptotic length (L) and growth coefficient (K) of Von Bertalanffy growth parameters (Gayanilo et al., 2005).

Lt = L∞[1-exp(-K(t-t0))] (6)

where Lt = growth at time t; L∞ = asymptotic length (mm); K = growth coefficient; t = age of fish; and t₀= fish age at which theorganism will have a zero length.

The growth performance index (ɸ) was calculated (Pauly & Munro, 1984) as follows:

ϕ=Log(K)+2log(L∞) (7)

Where Phi (Ø) = growth performance index; L∞ = Asymptotic length; K = Growth coefficient.

The age at zero length (to) was calculated from Pauly’s empirical equation (Pauly 1979) given below:
Log(-to)= -0.392-0.275LogL∞-1.0381*LogK (8)

The longevity (t_max) was estimated (Pauly, 1980) as:

T_max=3/K (9)

2.6| Estimation of Mortality Rates
The total instantaneous mortality (Z) was estimated using the FiSAT II software and a linearized length converted catch curve as defined by (Pauly 1984) and (Gayanilo et al. 2005).

ln(N_i/ Δt) = a + b*t_i(10)

where Ni is the number of fish in the length class I; Δt is the time needed for the fish to grow through length class i; ti is the absolute age (or the relative age) corresponding to the mid length of class i.

The natural mortality (M) was calculated using the empirical formula of Pauly (1980), as follows:

ln(M) = -0.0152-0.279In(L∞)+0.6543In(K)+0.4634In(T) (11)

where T = average annual temperature (28.4°C) of the water in which the fish lives.

Fishing mortality (F) was deduced by (Pauly 1980) as the difference between Z and M.

F=Z-M (12)

The exploitation ratio (E) was determined utilizing the quotient between total mortalities and fishing (Pauly 1984):

E=F/Z (13)

E values near to 0.5 are thought to represent an ideal degree of exploitation, whereas E values more than 0.5 indicate overexploitation (Tesfaye and Wolff 2015).

From the necessary data collected, a length structured virtual population analysis (VPA) was performed, as well as cohort analysis according to the FAO FiSAT-II programme (Gayanilo et al. 2005).

2.6|Recruitment Pattern, Relative Yield per Recruit (Y/R) and Biomass per Recruit (B/R)

As inputs, we used the VBGF parameters L, K, and to. Backward projection of frequencies onto the time axis of a data set following a VBGF trajectory resulted in charts displaying seasonal recruitment trends into the fishery (Dadzie et al., 2007). Moreau and Cuende both mentioned the usage of restructured samples (1991). The knife-edge selection procedure was used to estimate Beverton and Holt's relative yield per recruitment (Y/R) and relative biomass-per-recruitment (B/R) as a function of E (exploitation rate), using the model of Beverton and Holt (1966) as modified by (Pauly and Soriano, 1986) and incorporated into the FAO FiSAT II.

The ability to understand fish dynamic factors, particularly growth patterns, is critical for the long-term management of harvested populations. Fish growth rate plays a role in achieving the best results in fish farming. We presented results on growth and mortality rates, as well as exploitation rates, for five fin fish species taken in Cochin Estuary in this study. These characteristics were examined using length frequency data.

3.1| Length Composition, Length-Weight Relationship and Condition Factor (K)

The recorded mean size (32.37 cm) for P.planiceps in this study was significantly bigger than the 18.77 cm measured by Mon et al. for the same species in Mawlamyine, Myanmar (2020). In this study, the recorded mean size (6.58 cm) for D.insidiator was greater than the 4.58 cm recorded by Shoba for the same species from the south east coast of India (2014). Abhraham et al.(2011) reported that the yearly length compositions of this species ranged from 47 to 111mm. The dominant length group in our analysis was species with lengths ranging from 45 to 49 mm (8.06 percent). According to Karna et al.(2017), the yearly length compositions of C.praesteus ranged from 68 to 248 mm. The dominant length group in our analysis was species with lengths ranging from 80 to 89 mm (16.47 percent). Murugan et al.(2014) showed that the yearly length compositions of M.cephalus ranged from 100 to 540 mm. The dominant length group in our analysis was species with lengths ranging from 131 to 140 mm (16.43 percent).(Table,1)

The length-weight relationship demonstrated that M.cephalus experienced a negative allometric growth in this research. When b equals 3, the fish develops in an isometric manner; when b is not equal to 3, the fish grows in an allometric manner (Tesch 1971). Normally, the b value is 3.0, however it may be anything between 2.5 and 3.5. (2006) (Froese). B levels in fish differ according on sex, age, season, physiological conditions, growth increment, and nutritional status (Ricker 1975; Bagenal and Tesch 1978).Negative allometric growth for M.cephalus has also been found by Sahoo et al. (2012) and Murugan et al. (2014) in several water bodies throughout the world. However, (Rekha et al.,2021) observed positive allometric growth values for this species, with b values of 3.33. This disparity in results with other authors could be attributable to environmental and biological factors such as seasons, feeding behaviour, food competition, and maturation phases.

The value of b found in this study implies that the body form of big specimens got more elongated or that small specimens were in better nutritional condition at the time of sampling, according to Froese's findings (2006). In our study, the length-weight relationship revealed that C.praesteus developed at a positive allometric rate (b=3.38).

When b equals 3, the fish develops in an isometric manner; when b is not equal to 3, the fish grows in an allometric manner (Tesch, 1971). The aforementioned species, according to Karna et al.(2017), has positive allometric growth. In this study, D.insidiator grows at a negative allometric rate. Though negative allometric growth was observed in the same species by Krishna et al. (2015) and Srihari et al. (2015), (2018), the length-weight relationship demonstrated that P.planiceps had a negative allometric growth in this research. According to Srihari et al.(2018), the same species shows negative allometric growth.

The use of condition factor indices as a measure of overall health has been around for a long time (Brown and Murphy, 1991).

Values of the species condition factor that are less than one are regarded low, while those that are larger than one are considered high (Mir et al., 2012). According to Le Cren, environmental factors, food supply, and parasitism all have a substantial influence on fish health (1951).The condition factor observed for M.cephalus in this study shows that environmental factors, eutrophication, and increased environmental stresses favour its ill health. This value is lower than Guisse et al. (2021) and Dewiyanti et al. (2020) values from various research on the same species. The observed value of K for C.chanos was 0.83, which is between Biswas et al. (2011) observed value of K (0.67-0.93). This implies that little people develop more quickly than their larger counterparts. Though Abraham et al. (2011) discovered that K value for D.insidiator ranged from 1 to 1.2, the current investigation discovered that K value was 2.81. The k value discovered for C.praesteus was 1.3, implying robustness and well-being of the fish. The condition factor observed for P.planiceps in this study shows that environmental factors, eutrophication, and increased environmental stresses favour its ill health. This value is lower than values from different studies on the same species, Pramanick et al.(2017) .

3.2|Growth Parameters
The K-scan technique in ELEFAN I calculated an asymptotic length (L) of M.cephalus that was smaller than reports from (Murugan et al., 2014), (Sahoo et al., 2012) from the Chilka lagoon, India (60.6cm), (Bekova et al., 2019) from the Bay of Burgas, (Wokoma et al., 2001) from the Bonny Estuary D.insidiator's calculated asymptotic length was substantially smaller than, Abraham et al (2011) reported, value for the same species. The asymptotic length (L) of P.planiceps was determined using the K-scan technique in ELEFAN I, which was higher than reports from Mawlamyine, Myanmar (Mon et al., 2020) and Chilka lagoon (Panda et al., 2018). C.chanos' asymptotic length was discovered to be 421.05 mm. The Von Bertalanffy curvature parameter, K (Beverton and Holt, 1959), has been connected to fish lifetime, and mortality has been associated to longevity (Holt, 1965; Saville, 1977). Fish with a high K value also have a high M value, but fish with a low K value don't. A slow-growing species (low K) will go extinct quickly if natural mortality is higher (Sparre and Venema, 1998). This research's M.cephalus growth coefficient (1.4) is greater than values published by other authors in different study locations, such as (Panda et al., 2018; Sahoo et al., 2012; Murugan et al., 2014).

The growth coefficient for P.planiceps determined in this study is lower (0.41) than other authors' reported values in different study areas, such as (Panda et al., 2018; Mon et al., 2020), which are 0.8 and 0.99, respectively. Low growth rates and significant asymptotic lengths imply that fish species in these waters do not mature young and have a longer life span (Sparre and Venema, 1998). D.insidiator's growth coefficient (0.81) is comparable to the value discovered by Abraham et al (2011). According to Sparre and Venema (1998), K = 1.0 indicates rapid growth, K = 0.5 indicates medium growth, and K = 0.2 indicates moderate growth. C.chanos has a growth coefficient of 0.71, showing that it is a medium-growing species, whereas C.praesteus (K=1.3) is a fast-growing fish. Its short life span of 2.31 years confirms this.

The age at zero length for M.cephalus found in this work seems to be different from Panda et al. (2018) (-0.097), Murugan et al. (2014), and Wokoma et al (2001). The results obtained in this investigation for P.planiceps appeared to be equivalent to those reported by Panda et al. (2018) (-0.105), Mon et al (2020). For the other three species, t0 ranged from -0.078 to -0.14.

The value of (') for M.cephalus in this study indicates that the species had a lower growth performance index than most other studies, such as those from India's chilka lagoon (Panda et al. 2018) and India's parangapaettai water (Panda et al., 2018), (Murugan et al., 2014). P.planiceps had a somewhat lower growth performance index (2.81) than most other studies, such as those from India's Chilka lagoon (Panda et al., 2018), and Mayanmar's Mawlamyine (Mon et al., 2020). D.insidiator followed the same trend, with the value of the growth performance index being lower than in the prior study (Abraham et al., 2011). Exogenous and endogenous factors have an impact on fish growth and longevity (Wootton, 1998). The environment has an impact on a species' growth performance index. The researchers' adoption of diverse age reading approaches might explain the disparities in growth performance.

When using FiSAT to predict growth parameters, there are certain uncertainties since various combinations of the L and K variables might provide the same results (Moreau et al., 1986; Pauly and Morgan, 1987). When L is high, the K is low; when L is low, the K is high; when L is high, the K is low (Mirza et al., 2012; Murugan et al., 2014).

The above results show that growth characteristics for the same species found in different water bodies vary greatly. However, (Bartulovic et al., 2004 and Gulland, 1970) reported that there must be some differences in growth characteristics among localities due to the diversity and availability of dietary items, as well as hydrographical and climatic conditions..

3.3| Recruitment Pattern

The recruitment pattern of P.planiceps shows that recruitment attained its peak in August (18.33%) and that it happens particularly in the months starting from July to October. For D.insidiator , recruitment attained its peak in July (30.19%) and that it happens particularly in the months starting from May to August. For C.praesteus , recruitment peak had been observed in the month of August (28.81%) and recruitment time span observed from May to October. For M.cephalus peaked recruitment had been observed in the month of August (30.45%) which differs from the study observed by Panda et al (2018) on same species. C.chanos show the peak recruitment in the month of July though the bulk recruitment happens between the months of April to September.

Knowledge of the population parameters is required for the conservation of inland fisheries resources. In this study, the winter season had the lowest recruitment. Low recruitment during the winter season can be ascribed to rising temperatures, which often reached higher levels with the start of the peak summer season. Increased growth rates result in higher recruiting rates during this time period.

3.4|Mortality Rates (Z, M, F)
The rates of growth and death are linked. Fish growth influences their susceptibility to predation and fishing, as well as determining their dietary requirements (Pauly, 1984; Allen and Hightower, 2010). The empirical method for determining natural mortality (M) developed by Pauly has been frequently used (Sparre and Venema, 1998; Gayanilo et al., 2005). The natural mortality coefficient of a fish, according to Beverton and Holt (1959), is proportional to its growth coefficient (K) and inversely related to its asymptotic length. The length converted catch curve approach necessitates that the data set used to calculate total mortality Z adhere to the fundamental premise of stock equilibrium.

This assumption may be broken in a decreasing stock since a dropping trend in recruiting tends to under estimate Z by about the same amount as the loss in the stock (Al-Hosni and Siddeek, 1999; Hashemi et al., 2014).In Estuary Chilka(India), the total (Z), natural (M) and fishing mortality (F) rates for M.cephalus were Z = 2.6/year, M = 0.9/ year, and F = 1.7/year, respectively (Panda et al., 2018); in the Bardwali lagoon ,Egypt (Mehanna et al., 2013) reported for the same species Z = 1.73/year, M = 0.66/ year, and F = 1.07 /year, respectively; and in the same light, Murugan et al (2014) from Prangapettai ,India reported Z = 2.33/year, M =1.55 / year, and F = 0.78 /year, respectively for the same species. In Mawalamyine, Mayanmar, the total (Z), natural (M) and fishing mortality (F) rates for P.planiceps were Z = 1.97/year, M = 1.78/ year, and F = 0.19/year, respectively (Mon et al., 2020), in the Chilka lagoon, India (Panda et al., 2018) reported for the same species Z = 1.59/year, M = 0.64/ year, and F = 0.95 /year where the observed value for same species in this study was Z = 0.89 /year, M = 0.49/ year, and F = 0.4 /year. (Figure,2).For other three species, observed fishing mortality is higher than natural mortality which implifies the stock is overharvested (Gulland, 1971). Accordingto Gulland (1971), F and M should be same for a well-managed stock. The higher fishing mortality over natural mortality rate indicated overharvesting of fishes.Similar findings have been made by other researchers in international waterways (Aleleye-Wokoma et al., 2001; Glamuzina et al., 2007; Moorthy, Reddy & Annappaswamy, 2003). The same species may have different rate of natural mortality in different locations, depending on the density of debris and prey whose affluence is influenced by fishing activities (Murugan et al. 2014; Rahman et al. 2016).

3.5| Virtual Population Analysis (VPA), Yield-per-Recruit and Biomass-per-Recruit and Exploitation Rates

The estimated virtual population of this commercial fish species was constructed using Von Bertalanffy values, mortalities, and the length-weight connection. The length structured VPA analysis revealed that fishing mortality rates vary in response to average length and fluctuated widely throughout the fish's lifespan (Mirza et al., 2012).

All five fin fish species have a current exploitation rate that was higher than the predicted Emax value from FiSAT II output. The estimate of E for sustainably managed resources should be 0.50. (Gulland, 1971). Patterson (1992) stated that E should be kept around 0.4 for optimal pelagic fish population utilisation. Panda et al. (2018) found that the M.cephalus in India has an exploitation rate E greater than E max (E = 0.66, indicating that the species is overexploited), which could lead to the stock's collapse in the future if serious management measures are not taken.

In the current study, an exploitation rate E greater than E max was discovered for M.cephalus in Cochin estuary Estuary (E = 0.66,Emax=0.42, indicating that the species is extremely overexploited), which could lead to the stock's decrease in the future if serious management measures are not taken. According to Panda et al. (2018), exploitation between 0.4 and 0.5 is considered overexploited, 0.5-0.6 is considered moderately overexploited, and greater than 0.6 is considered very overexploited. The exploitation rate for P.planiceps was reported to be between 0.6 and 0.1 by Panda et al. (2018) and Mon et al. (2020), which differs from the findings of this study (0.45). Apart from P.planiceps (which was severely overexploited), all four species in this study were severely overexploited.(Figure,3)

The paucity of data on the demographic characteristics of many threatened or exploited fish stocks makes it difficult to create effective fishery management plans. The current study is the first to publish information on the population characteristics of all five species found in this estuary, and it can be used as a starting point for determining exploitation limits and a conservation strategy for the species, which shows a declining population trend in its native range.

As a result, there is an immediate need to reduce fishing effort and reduce the mesh size of fishing gear in order to avert the extinction of this critically endangered fish species. The recent findings should contribute in ecosystem-based fisheries management (EBFM) research and, as a consequence, encourage responsible Cochin estuary fishery management strategies. A comprehensive analysis of the species' population genetic structure over its entire range is also necessary, since this will help in the future adoption of successful conservation measures.

Ethical Approval and Consent to participate

Not applicable

Human and Animal Ethics

Not applicable

Consent for publication

I,the undersigned give my conset for the publication of the manuscript

Availability of supporting data

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

On behalf of all authors, the corresponding author states that there is no conflict of interest

Funding

Not applicable

Authors' contributions

Snigdhodeb Dutta wrote the main manuscript as well as generated the figures.

ACKNOWLEDGEMENT

The author would like to express his gratitude to the Director of the Cochin University of Science and Technology's School of Industrial Fisheries for providing the required facilities for conducting research at the school.

Authors' information

Snigdhodeb Dutta

Department of Industrial Fisheries,

Cochin University of Science and Technology,India

Kerala – 682022.

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Table 1: Growth, Condition factors and Mortality parameters of examined species from Cochin Estuary, India

Population parameter	Value of M.cephalus	Value of C.chanos	Value of P.planiceps	Value of C.praesteus	Value of D.insidiator
Asymptotic length (L∞; mm)	169.05	421.05	400.05	147	110.25
Growth coefficient (K; year−1)	1.4	0.71	0.41	1.3	0.81
Growth performance index (ϕ)	4.6	5.09	4.81	4.45	3.99
Longevity (3 K−1; years)	2.14	4.23	7.31	2.31	3.7
Total mortality rate (Z; year−1)	3.45	2.27	0.89	4.04	2.72
Natural mortality rate (M; year−1)	1.38	0.69	0.49	1.37	1.09
Fishing mortality rate (F; year−1)	2.07	1.58	0.4	2.67	1.63
Fulton’s condition factor	1.69±0.13	0.83±0.04	0.92±0.19	1.3±0.16	2.81±0.36
Modified condition factor	3.60±0.2	2.34±0.12	1.78±0.34	0.54±0.04	4.94±0.41
Relative condition factor	1.02±0.05	1.06±0.05	1.04±0.2	1.01±0.08	1.008±0.08
Exploitation (E; year−1)	0.6	0.7	0.45	0.66	0.6
E⁵⁰	0.28	0.28	0.27	0.27	0.26
E_max	0.42	0.424	0.41	0.41	0.4

No competing interests reported.

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Growth, mortality and stock status of five fin fish species from a tropical estuary Snigdhodeb Dutta Department of Industrial Fisheries, CUSAT, Kerala –682022

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