Corresponding author: Tuantong Jutagate ( tuantong.j@ubu.ac.th ) Academic editor: Sanja MatićSkoko
© 2021 Penprapa Phaeviset, Pisit Phomikong, Piyathap Avakul, Sontaya Koolkalaya, Wachira Kwangkhang, Chaiwut Grudpan, Tuantong Jutagate.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Phaeviset P, Phomikong P, Avakul P, Koolkalaya S, Kwangkhang W, Grudpan C, Jutagate T (2021) Age and growth estimates from three hard parts of the spotted catfish, Arius maculatus (Actinopterygii: Siluriformes: Ariidae), in Songkhla Lake, Thailand’s largest natural lake. Acta Ichthyologica et Piscatoria 51(4): 371378. https://doi.org/10.3897/aiep.51.74082

The spotted catfish, Arius maculatus (Thunberg, 1792), is a euryhaline fish that is economically important in the IndoWest Pacific. Population dynamics studies and stock assessments of this species have focused on marine stocks, but not those from fresh water. In this study, the age and growth of A. maculatus were, therefore, investigated for the inland stock in Songkhla Lake, Thailand. A total of 213 individuals ranging between 35 and 238 mm TL were used. The length–weight relation indicated positive allometry of this population. Three hard parts (otolith, dorsal and pectoralfin spines) were used for aging. The marginal increment ratio confirmed that an annulus was deposited once a year in all three hard parts. All of the samples were aged between 0+ and 6+ years. Verification of age estimates from three readers showed that the otolith was the most suitable part for age estimation. Three growth models (von Bertalanffy, Gompertz, and logistic) were applied in the study. The von Bertalanffy model best described the growth of this fish in Songkhla Lake. The obtained asymptotic length was 290.87 mm TL and the relative growth rate parameter was 0.166 year^{–1}. Our results will be applied as inputs for fish stock assessment models. The obtained growth parameters also can serve as a reference for A. maculatus stocks elsewhere.
Arius maculatus, Dorsalfin spine, otolith, pectoralfin spine, Thailand, von Bertalanffy growth model
The family Ariidae accommodates more than 140 species of catfishes, found mainly in marine and brackish waters (
Effective fisheries management requires an understanding of the stock status and population dynamics of the targeted fish species, where the growth parameters (i.e., asymptotic length and curvature parameter) are among the crucial inputs (
Along with selecting suitable hard parts for aging, the most suitable model must be chosen for the lengthatage key, which can be determined by the shape of the growth curve and error component of the data (
The aim of this study was to provide lengthatage data and a growth model for the A. maculatus stock in Songkhla Lake, Thailand. We assess the suitability of hard parts for aging and evaluate three growth models to describe the relation between age and length, which can be used as a reference for other A. maculatus stocks. The results are also expected to be further used for stock assessment and fisheries management of the stock in Songkhla Lake for its sustainable exploitation.
Study area and fish sampling. Songkhla Lake (Fig.
At the fisheries center, the largest pair of otoliths (i.e., lapilli) were removed, washed, and kept dry in a vial. Dorsal and pectoralfin spines were cut with bonecutting forceps. Each hard part was embedded in resin and cut by a lowspeed diamond saw (South Bay Technology Inc., model: 650). Bony parts were then polished by sandpaper (grit size ranging from 600 to 1500) until the core was seen. Each polished sample was photographed under 40× magnification, and annual rings were counted visually from the monitor and using the ImageJ program. An annual ring was considered as the boundary between the inner edge of a wide opaque zone (i.e., corresponding to high growth rate) and the outer edge of a narrow translucent zone (i.e., corresponding to low growth rate) (
Data analysis. Length–length relations and length (TL)–weight relation (LWR) were examined by linear and curvilinear regressions. The estimated parameter “b” from LWR was tested for significant deviation from 3 by using a ttest. Annual ring formation was validated by marginal increment ratio (MIR) analysis (
$\mathrm{MIR}=\frac{\left(R{R}_{n}\right)}{\left({R}_{n}{R}_{n1}\right)}$
where R is the radius (distance between the center and the edge of hard part), R_{n} is the distance from center to outer edge of last complete band, and R_{n –} _{1} is the distance from center to outer edge of nexttolast complete band. The difference in MIR among months of sampling was tested by Kruskal–Wallis test, and Dunn’s post test was applied when a significant difference was found at α = 0.05.
The percentage of agreement (PA) among the three readers was calculated as the ratio of the number of agreements among the three readings to the total number of readings made. Precision in age reading among the three readers of each hard part was tested by two methods (
$\mathrm{MPE}=\frac{{\sum}_{j=1}^{n}{\mathrm{MPE}}_{j}}{n}$
where
${\mathrm{MPE}}_{j}=100\times \frac{{\sum}_{i=1}^{R}\frac{\left{x}_{ij}{\overline{x}}_{j}\right}{{\overline{x}}_{j}}}{R}$
where MPE_{j} is the mean percentage error for the j^{th} fish, x_{ij} is the i^{th} age estimate of the j^{th} fish, x̅_{j} is the mean age estimate for the j^{th} fish, R is the number of times that each fish was aged, and n is the number of samples.
$\mathrm{CV}=\frac{{\sum}_{j=1}^{n}{\mathrm{CV}}_{\mathrm{j}}}{n}$ where ${\mathrm{CV}}_{j}=100\times \frac{\sqrt{\frac{{\sum}_{i=1}^{R}{\left({x}_{ij}{\overline{x}}_{j}\right)}^{2}}{R1}}}{{\overline{x}}_{j}}$
where CV_{j} is the coefficient of variation for the j^{th} fish. The age readings of each fish sample and each hard part from the three readers were then averaged and rounded to the nearest integer. Two additional readers with extensive experience in fish aging reviewed each hard part and checked its designated age. The agebias plot (
L_{t} = L_{∞} (1 − e^{−k (t − t}0^{)})
von Bertalanffy model
${L}_{t}={L}_{\infty}{e}^{{e}^{k\left(t{t}_{0}\right)}}$
Gompertz model
L_{t} = L_{∞} (1 + e^{−k (t − t}0^{)})^{−1}
Logistic model
where L_{t} is the predicted lengthatage t, L_{∞} is the asymptotic length, k is a relative growth rate parameter, and t_{0} is the age when length is theoretically zero. The growth models were fitted to lengthatage data using nonlinear leastsquares. The residual sum of squares (RSS) was used to measure the discrepancy between the data and an estimation model. The growth performance index (Ø′,
Ø′ = log(k) + 2log(L_{∞})
The obtained L_{∞} [cm] and k values were further used to estimate the natural mortality coefficient (M) by using Pauly’s equation (
log_{10}M = −0.0066 − 0.279log_{10}L_{∞} + 0.6543log_{10}k + 0.4635log_{10}T
where T is the annual mean water temperature, which was set at 30°C (International Lake Environment Committee Foundation 2021). Data analysis was conducted by using Rstatistics (
A size distribution of the 213 A. maculatus samples used in this study is presented in Fig.
The 60 A. maculatus samples for the MIR study ranged between 35 and 238 mm TL, with the mean ± SD of 128 ± 43 mm TL. The MIR results showed clear increasing trends from January (MIR near 0.5) to October (MIR near 1.0) in all three hard parts, and a significant difference was found between January and the other sampling months (P < 0.05; Fig.
Age estimates from the three hard parts ranged from less than 1 to a maximum of 6 years (Fig.
Precision in age reading among three readers of Arius maculatus sampled from Songkhla Lake, Thailand.
Hard part  Agreement  MPE  CV 

Otolith  58.2%  9.5%  12.6% 
Dorsalfin spine  50.3%  15.5%  16.0% 
Pectoralfin spine  55.9%  12.6%  14.4% 
Observed age from three hard parts of Arius maculatus from Songkhla Lake, Thailand.
Length (TL) [mm]  Age [year]  Total  

0+  1+  2+  3+  4+  5+  6+  
31–50  11, 11, 11  33  
51–70  10, 10, 11  1, 1, 0  33  
71–90  19, 20, 21  18, 17, 16  111  
91–110  0, 0, 1  11, 15, 14  5, 2, 2  1, 0, 0  51  
111–130  12, 13, 16  22, 20, 20  3, 4, 1  111  
131–150  1, 1, 2  13, 20, 17  13, 7, 9  1, 0, 0  84  
151–170  0, 0, 1  2, 8, 6  23, 22, 22  9, 5, 6  105  
171–190  0, 2, 4  9, 16, 11  16, 10, 13  3, 1, 1  1, 0, 0  87  
191–210  0, 2, 1  2, 2, 3  1, 1, 1  2, 0, 0  15  
211–230  1, 1, 1  1, 1, 1  6  
231–250  0, 1, 1  1, 0, 0  3 
Age bias plots between hard parts of Arius maculatus from Songkhla Lake, Thailand. (A) otolith vs. dorsal spine, (B) otolith vs. pectoral spine and (C) pectoral spine vs. dorsal spine. Each error bar represents the 95% confidence interval. Red error bar indicates a significant difference in age agreement between two hard parts.
Parameter estimation in all models using the observed lengthatage data from the three hard parts is displayed in Table
Growth parameters from three hard parts of Arius maculatus from Songkhla Lake, Thailand.
Model  L_{∞} [mm]  k [year^{–1}]  t _{0} [year]  RSS  Ø′  M [year^{–1}] 

Otolith  
von Bertalanffy  290.87  0.166  –1.51  58,029  2.148  0.573 
Gompertz  229.82  0.383  0.598  57,467  2.306  1.058 
Logistic  209.85  0.604  1.274  57,472  2.425  1.463 
Dorsalfin spine  
von Bertalanffy  292.20  0.184  –1.34  65,824  2.196  0.612 
Gompertz  226.19  0.443  0.507  65,267  2.355  1.170 
Logistic  205.21  0.706  1.072  65,211  2.473  1.631 
Pectoralfin spine  
von Bertalanffy  286.08  0.189  –1.389  59,626  2.189  0.627 
Gompertz  226.11  0.435  0.451  59,753  2.347  1.156 
Logistic  206.28  0.685  1.039  60,288  2.464  1.595 
L_{t} = 290.87(1 − e^{−0.166(t + 1.51)})
von Bertalanffy model
${L}_{t}=229.82{e}^{{e}^{0.383(t0.598)}}$
Gompertz model
L_{t} = 209.85(1 + e^{−0.604(t − 1.274)})^{−1}
Logistic model
By applying the growth curves of the three models (Fig.
The age–length key and growth estimation of fishes and shellfishes can provide valuable insight into their life history and be further used to prescribe optimum fishing regulations for sustaining their fisheries (
The maximum length of A. maculatus in this study was 23.8 cm TL, which is similar to the maximum size from the Mekong Delta (25 cm TL;
Age validation through MIR analysis provides relative certainty of annulus formation and confirms the growth zone deposition in A. maculatus. Low MIR was found in the early part of the year (January, winter) when the water temperature in the inner zone is normally less than 27°C; it is around 30°C during the rest of the year (
The growth models based on the age–length key from otolith reading revealed a lower residual sum of squares than for spines, which confirms the suitability of this hard part for A. maculatus aging. Higher L_{∞} and lower k values from the von Bertalanffy model than from other growth models have been reported in many studies (e.g.,
The age–length relation and growth of A. maculatus in Songkhla Lake, Thailand, were examined by using hard parts. Different aging structures and growth models were compared for biases in the results. Results showed that otoliths provided more precise age estimation among three readers. The von Bertalanffy model was judged to be the most suitable candidate for growth estimation because of the lowest residual sum of squares. The obtained growth parameters (i.e., L_{∞} and k) can be used as inputs in other fish stock assessment models (e.g., the yield per recruit model) to investigate the optimum fishing level for this stock, which currently experiences high fishing pressure.
The first author is grateful for financial support towards his master’s degree study from the Agricultural Research Development Agency (ARDA) Grant HRD6405081.