*
Corresponding author: Hartati Kartikaningsih
Copyright ©
2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0.
Analysis of population dynamics for the sustainability of yellowfin tuna (Thunnus
albacares) resources landed in Pondokdadap Sendangbiru, Malang District, Indonesia
Agus Tumulyadi
1, Hartati Kartikaningsih
2, 3, *, Bambang Semedi
1, 2, Alda Wuldan Dwigita
1 and Abd. Aziz
Amin
3
1
Department of Utilization of Fisheries and Marine Resources, Faculty of Fisheries and Marine Sciences, Brawijaya
University, Malang, Indonesia.
2
Management of Environmental Resources and Development Study

Program, Graduate School of Brawijaya University,
Malang, Indonesia.
3
Department of Fisheries and Marine Resources Management, Faculty of Fisheries and Marine Sciences, Brawijaya
University, Malang, Indonesia.
International Journal of Life Science Research Archive, 2023, 04(02), 031– 038
Publication history: Received on 08 February 2023; revised on 08 April 2023; accepted on 10 April 2023
Article DOI: https://doi.org/10.53771/ijlsra.2023.4.2.0050
Abstract
Pondokdadap is a fishing port that has large pelagic fish resources and biodiversity. This fishing port is an effective
location for fish landing where it is close to the yellow fin tuna fishing ground. Tuna is one of the important commodities
in the national capture fisheries sub-sector. Yellow fin tuna (Thunnus albacares) is a fish with economic value and a high
level of public consumption, both domestically and abroad. However, it has an impact on the magnitude of market
demand which is in line with the high activity of catching tuna in nature. Therefore, the exploitation of yellowfin tuna
resources should be controlled. The purpose of this study was to identify, growth rate, mortality rate, exploitation rate
and recruitment pattern of yellow fin tuna (Thunnus albacares) landed at TPI Pondokdadap. The results of growth
parameter values, L∞= 191 cm, K = 0.36 per year, and t0 = -0.27 per year. The mortality rate is Z = 2.81 per year, M =
0.45 per year, F = 2.36 per year and the exploitation rate is E = 0.84 per year, which means that the utilization status of
yellowfin tuna (Thunnus albacares) was in the overexploited category. The highest value seen from the analysis of
recruitment patterns occurred in June with a percentage of 34.83%. This research concludes that advisable to limit the
efforts of catching yellow fin tuna (Thunnus albacares).
Keywords: Thunnus albacares; Pondokdadap; Fishing ground; Population
1 Introduction
Sendang Biru waters is a strategic area as an abundant fishing ground. Sendang Biru has the potential to bring in a wide
variety of fish types from open waters, with tuna being one of the important commodities from the national capture
fisheries sub-sector. As a result of increasing market demand each year, tuna is the leading export fish with an average
catch percentage of 64% per year [1, 2]. Yellow fin tuna (Thunnus albacares) is a consumption fish that belongs to the
Scombridae family [3]. Yellow fin tuna (Thunnus albacares) is a migratory fish species that inhabits the epipelagic zone
to a depth of 200 meters below sea level and is commonly found in tropical and subtropical marine waters throughout
the world [4]. In general, this fish is caught by fishing gear including hand line, troll line, and longline line. In small-scale
fisheries, fishery business actors catch tuna using hand line fishing gear or handlines. The fishermen in Sendang Biru
catch yellow fin tuna using a fleet of lifeboats with fishing gear in the form of hand lines which are operated around the
Indian Ocean in the waters around deep sea FADs [5]. The average yellowfin tuna (Thunnus albacares) caught in the
waters of the Indian Ocean in 2016-2020 was 434.235 kg per year, and during that period the status of yellowfin tuna

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
32
(Thunnus albacares) was at the IOTC (Indian Ocean Tuna Commission) classified as overfishing. The high level of
exploitation due to overfishing of yellowfin tuna makes it vulnerable to unsustainable fishing [6].
The dynamics of fish stock populations is very important to study because it is quite closely related to a statement or
claim for sustainable fish resources in an area. Studies on the dynamics of fish populations can determine decision
making regarding the management of fish resource stocks, and can also provide indications through biological
indicators in determining stock status [7]. Population parameters of a type of fish are studied in order to obtain
information about stocks so that fishery resources can be used optimally and sustainably [8, 9]. Due to the higher
demand for yellowfin tuna on the international market in recent years, this has had an impact on intensive exploitation,
as well as on the availability. Therefore, to find out information on the status of yellowfin tuna stocks, it is importat to
conduct research on biological aspects including growth rates, mortality rates, exploitation rates, and patterns of
recruitment of new individual fish using the fork length variable of yellowfin tuna. It aims to find enough information
about the sustainability of yellowfin tuna stocks in the Indian Ocean region now and in the future [10].
Therefore, to find out information on the status of yellowfin tuna stocks, it is necessary to conduct research on biological
aspects including growth rates, mortality rates, exploitation rates, and patterns of recruitment of new individual fish
using the fork length variable of yellowfin tuna. It aims to find enough information about the sustainability of yellowfin
tuna.
2 Material and methods
2.1 Location of Research
The research location is located at UPT PPP Pondokdadap Fish Auction Place, Sendangbiru Hamlet, Tambakrejo Village,
Sumbermanjing Wetan District, Malang Regency, East Java Province, Indonesia in 2022. Retrieval of long distribution
frequency data of yellowfin tuna was carried out every month with a minimum collection period of 30 days.
2.2 Analysis of Fish Length Frequency Distribution Data
To determine the frequency of fish length, the data used is fork length (FL) data from yellowfin tuna (Thunnus albacares).
The process of analyzing the long frequency distribution includes determining the desired number of class intervals,
determining the class range, determining the class frequency, then entering length measurement data for the same class
interval, after that a plot is made on the graph. The purpose of making the graph is to find out the shift in the distribution
of long classes in each month during the study. Shifts in the length distribution can indicate the number of existing
cohorts (age groups). It can be seen in the graph that the mode of the long class frequency distribution shifts every
month, indicating the existence of several cohorts.
2.3 Growth rate
Measurement of growth rate used the bhattacharya method. This method is a method for analyzing the structure of an
age group, which uses the separation of the combined distribution into several separate normal distributions that can
represent an age group (cohort). In determining the normal distribution starting from the left side of the total
distribution. Likewise with the normal distribution that has been determined, it will be separated from the total
distribution and then the same steps are repeated as long as it is still possible to do it in separating other normal
distributions from the total distribution. The peak of the normal distribution is called the cohort (age group) or the
mode of the long frequency each month. The mode value is used to calculate L∞ and K. To estimate the growth
parameters from the L∞ (asymptotic width) and K (growth constant) values of a fish stock, the Fisheries Stock
Assessment Tools II (FISAT II) application was used.
The L∞ and K values that have been obtained, then for estimating the theoretical age (t0) of fish when the length is
equal to 0 (zero) years Pauly's empirical equation is used [11], as follows:
Log (-t0) = 0,3922 – 0,2752 (Log L∞) – 1,038 (Log K)…………………..………..(1)
Information
L∞ : maximum length that can be reached by fish if no death occurs (cm)
K : Growth rate coefficient (per year)
T0 : The theoretical age of fish at zero length (per year).

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To determine the value of the growth rate of yellowfin tuna (Thunnus albacares), the Von Bertalanffy growth model
equation is used [11], which is as follows:
Lt = L∞ (1 – e [– K (t-t0)])…………………………………………………………....(2)
Information
Lt : length of fish at age t (cm)
L∞ : Maximum length that fish can reach when no death occurs (cm)
K : Growth rate coefficient (per year)
t0 : The theoretical age of fish at zero length (years)
t =Age of fish (years)
2.4 Mortality Rate
The results of estimating the value of the total mortality rate (Z) and the natural mortality rate (M) can determine the
fishing mortality rate (F). to determine the estimated value of total mortality (Z), that is by using analysis in the FISAT
II application.
Calculation of the total mortality rate (Z) uses the Beverton and Holt equation formula [11], which is as follows:
Z=K(
L∞−L
L −L′
) …………………………………………………………….…………….(3)
Information
K : Growth rate coefficient (per year)
L∞ : Asymptote length of fish (cm)
L : average length of fish caught (cm)
L' : the smallest limit for the length class of fish that has been fully caught

Calculation of the natural mortality rate (M) uses the Pauly empirical method [11], which is as follows:
M=0.8∗exp(−0.152−0.278 Ln L∞+0.6543 Ln K+0.4636 Ln T)…………….(4)
Information
M : Natural mortality rate (years)
L∞ : Asymptote length of fish (cm)
K : Growth rate coefficient (per year)
T : average surface water temperature (°C)

Calculation of fishing mortality rate (F) is determined from the estimation of Z and M values, then the following equation
is obtained:
Z=F+M atau F=Z−M ……………………………………………………………(5)
Information
E : Exploitation rate value
F : Fishing mortality rate value
Z : Total mortality rate value
2.5 Exploitation Rate
Exploitation rate (E) level of utilization of fishery stocks can be determined using the formula:
E=
F
F+M
=
F
Z
……………………………………………………………………...(6)
Information
F : fishing mortality rate

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
34
Z : Total mortality rate
M : Natural mortality rate
E : Exploitation rate

The criteria for determining the estimation of the status of fishery resources using the exploitation rate parameter are
as follows:
E > 0.5: indicating overexploited fisheries status
E = 0.5: indicates the status of the fishery is classified as optimal/Maximum Sustainable Yield (MSY)
E < 0.5: indicates the status of the fishery underexploited
2.6 Recruitment Pattern
Recruitment pattern analysis can be estimated using FISAT II software using the Recruitment Pattern sub menu. The
data needed are the values of L∞, K and t0 which were previously obtained. The results of the analysis are the histogram
graphs and the percentage of recruitment patterns every month.
3 Results and discussion
3.1 Fish Length Frequency Distribution
Forked length (FL) measurements of yellowfin tuna (Thunnus albacares) in Pondokdadap, a total of 4440 samples. From
the primary data, it is known that the range of fish length is from 27 to 171 cm using a class interval of 1 cm.
Furthermore, the data that has been obtained is processed in the FISAT II application using the Bhattacharya method
and several cohorts of fish are obtained in each month starting from Marchl to July 2022. The purpose of knowing the
distribution of fish length frequencies is to find out the normal distribution by looking at the number of cohort peaks
produced where it can be used to determine the maturity of the fish and the size feasible for catching yellowfin tuna.

Figure 1 Distribution of March-July 2022 Length Frequency



Mean
41.46 cm
MARET
APRIL
MEI JUNI
JULI
Mean
162.79 cm
Mean
160,20 cm
Mean
126.50 cm
Mean
153.44 cm
MARET
41,46 cm

APRIL
162.79 cm

March
May June
July

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
35
The distribution of the long frequency of yellowfin tuna (Thunnus albacares) shows a graph of the factors that affect the
distribution of the long frequency of yellowfin tuna [12]. Yellow fin tuna caught in the surface area of the waters and
still around the fishing ground is dominated by small fish (Juvenile) with a percentage of 98%. Based on the presumptive
assumptions, yellowfin tuna matured for the first time at 84 cm FL with a length of >84 cm FL classified as an adult fish
and <84 cm FL classified as a juvenile [13].
The results of this study were 1040 samples of yellowfin tuna with a fish length range of 28 to 162 cm and based on the
graph, there was a tendency to form 2 cohorts. In April 2022, the results obtained from a study of 852 samples of
yellowfin tuna with a fish length range of 29 to 171 cm and based on the graph there is a tendency to form 2 cohorts. In
May 2022, the results obtained from a study of 960 samples of yellowfin tuna with a fish length range of 28 to 170 cm
and based on the graph there is a tendency to form 7 cohorts. In June 2022, the results obtained from a study of 946
samples of yellowfin tuna with a fish length range of 27 to 167 cm and from the graph there is a tendency to form 7
cohorts. In July 2022, the results obtained from a study of 642 samples of yellowfin tuna with a fish length range of 27
to 167 cm and based on the graph there is a tendency to form 6 cohorts.
The results of this analysis indicate that the long frequency distribution of yellowfin tuna in Pondokdadap is dominated
by small fish, namely fork lengths of less than 100 cm, but in certain months, such as May and July, medium to large fish
with a length of more than 100 cm begin to dominate. The decrease in the frequency of fish length is due to the increase
in body length in fish. This illustrates that yellow fin tuna in Pondokdadap dominated by large, medium to small fish.
3.2 Growth Rate Analysis
The results of the observations showed that the maximum length/ asymptote length (L∞) was 191 cm with a growth
coefficient (K) of 0.36 per year. Then the value (t0) is the result that has been calculated by Pauly's formula, which is
0.2758 per year. This value indicates that the yellowfin tuna (Thunnus albacares) can grow up to 191 cm if there is no
death due to fishing. The von Bertalanffy growth function (VBGF) shows that yellowfin tuna (Thunnus albacares) can
continue to grow up to an asymptotic length of 191 cm if they do not die due to natural factors or fishing factors.
Previous research in 2021 showed an L∞ value of 174.46 cm, a K value of 0.21 per year and a t0 value of 0.4951 per
year [15]. It is known that the maximum length (L∞), growth rate (K) and value (t0) of the research in that year were
smaller than the results of this study.
This study shows that the yellowfin tuna landed at Pondokdadap in 2022 requires a short time to reach its asymptotic
length because it has a higher coefficient value, on the other hand in 2021, yellowfin tuna takes a long time to reach its
asymptotic length due to the higher coefficient value. low. The value of the growth rate (K) is highly correlated with the
value (L∞) which means the value (K) can indicate the growth rate of the fish until it reaches (L∞). If the value (K) is
large, fish generally have a relatively short life span and vice versa (Mamangkey & Nasution, 2014). The growth rate (K)
value of yellowfin tuna is slow because it does not reach a value of one, meaning that the use of fishery resources must
be approached with the precautionary principle [14].

Figure 2 VBGF plots for Yellowfin Tuna (Thunnus albacares) in 2022
The values of L∞K and to that have been obtained are then used to determine the optimal growth point for yellowfin
tuna (Thunnus albacares) using the Von Bertalanffy and Beverton Holt equation. Based on research conducted in 2022,

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
36
the results obtained were Lt = 191 (1-e-0.36(t+0.2758)). Then for the optimal point of fish growth, the value of tmax =
0.85 years and length of Lmax = 181.45 cm are obtained. In the growth curve in 2022, it is known that when fish are 0-
8 years old they experience quite rapid growth, but when they are more than 8 years old the growth of yellowfin tuna
(Thunnus albacares) becomes slower and tends to be constant according to with a maximum length of fish that is equal
to 181.45 cm.

Figure 3 Yellowfin Tuna (Thunnus albacares) Growth Rate in 2022
3.3 Mortality Rate
The value of the total mortality rate was obtained by analyzing data using the FISAT II application in the Length-
converted Catch Curve sub-program by entering data on known L∞ and K values, so that the value of the total mortality
rate (Z) was 2.81 per year-1. The value of natural mortality (M) is calculated using the Pauly formula by entering the
values of L∞, K and also T. It is known that the value of T or the average temperature in Sendang Biru waters during the
study period from March to July 2022 was 29.18 °C, so that the natural mortality rate (M) is 0.45 per year. The value of
the fishing mortality rate (F) is obtained by calculating the formula for the total mortality rate minus the natural
mortality rate, so that the fishing mortality rate is 2.36 per year. Based on the results of the analysis, it is known that the
value of the fishing mortality rate is greater when compared to the natural mortality rate, which means that more deaths
of yellowfin tuna caused by fishing activities than deaths in nature.
3.4 Exploit Rate
Exploitation rate analysis is obtained from the calculation of mortality using the formula for the fishing mortality rate
(F) divided by the total mortality rate. Based on calculations, it is known that the value of the exploitation rate for
yellowfin tuna (Thunnus albacares) landed at Pondokdadap Sendang Biru in 2022 is 0.84 per year. This value has
exceeded the optimal limit of the exploitation rate, which is 0.5 per year. This indicates that the status of the yellowfin
tuna fishery, especially in WPP-RI 573 that year, was at the overexploited level. Based on the research that was carried
out in 2021, the calculation results for the exploitation rate (E) are 0.88 per year. This indicates that the status of
fisheries is at the overexploited level [15].
3.5 Recruitment Pattern Analysis
The recruitment pattern shows that the peak of fish recruitment occurred in June with a percentage gain of 34.83%.
Conversely, the lowest percentage occurred in December with a percentage value of 0%, this indicates that there was
no recruitment at all that month. Recruitment patterns are related to spawning time. It is known that in the study of
yellowfin tuna in Pondokdadap in 2021, results showed that the peak of fish recruitment occurred in August with a
percentage of 28.63% [15]. This shows a shift in fish spawning times and also differences in the percentage value of
recruitment when compared to the results of the analysis of the yellowfin tuna recruitment pattern in 2022. The cause
is due to natural factors, namely changes in weather anomalies due to shifts in the onset of the rainy season and dry
season due to the influence of El Nino Southern Oscillation (ENSO).





tmaks = 8.05 year
Lt = 191 (1-e
-0.36(t+0.2758)
)
Lmaks = 181.45 cm

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
37

Figure 4 Graph of Yellowfin Tuna (Thunnus albacares) Recruitment Pattern in 2022
4 Conclusion
This study concluded that the maximum length value for yellowfin tuna derived from the 2022 Forked Length (March -
July) data with L∞ is 191 cm, K is 0.36 per year, and t0 is -0.2758 years. The total mortality rate (Z) for yellowfin tuna
was 2.81 per year, the natural mortality rate (M) was 0.45 per year, and the fishing mortality rate (F) was 2.36 per year.
Analysis of the rate of exploitation of yellowfin tuna obtained the value of the exploitation rate (E) is 0.84 per year,
which means the value of E is more than 0.5. This shows the current utilization status is overexploited. Analysis of the
recruitment pattern of yellowfin tuna showed that the peak of recruitment occurred in June with a percentage of
34.83%. and the lowest recruitment value occurred in December with a percentage of 0%.
Compliance with ethical standards
Acknowledgments
This research is supported by the Research grant for the Postgraduate School Study Program, Brawijaya University
Malang for the 2022 fiscal year with contract number 2998/UN10.F40/PT01/2022.
Disclosure of conflict of interest
The authors declare that the research was conducted in the absence of any financial relationships that could be
construed as a potential conflict of interest.
References
[1] Nandita F, Setiawan B, Riana F. Sustainability Analysis of Tuna (Thunnus sp) Fihery in Sendang Biru, Malang
Regency. Economic and Social of Fisheries and Marine Journal. 2021: 9, 72-85.
[2] Wirawan RR, Anas P, Wisudo SH. Sustainable Tuna Fisheries Management Strategy In SendangBiru, Malang
Regency, Indonesia. The International Journal of Engineering and Science. 2020: 9 (2), 16-19
[3] Arrate IA, Fraile I, Crook DA, Zudaire I, Arrizabalaga H, Greig A, Murua, H. Otolith microchemistry: A useful tool
for investigating stock structure of yellowfin tuna (Thunnus albacares) in the Indian Ocean. Marine and
Freshwater Research. 2019: 70 (12), 1708–1721.
[4] Mayer F, Andrade H. Size of yellowfin tuna (Thunnus albacares) caught by pole-and-line fleet in the southwestern
Atlantic Ocean Size of yellowfin tuna (Thunnus albacares) caught by pole-and-line fleet in the southwestern
Atlantic Ocean. Brazilian Journal of Aquatic Science and Technology. 2008: 12, 59-62.
[5] Restrepo V, Dagorn L, Itano D, Justel-Rubio A, Forget F, Moreno G. (2017). A Summary of Bycatch Issues and ISSF
Mitigation Initiatives To Date in Purse Seine Fisheries, with emphasis on FADs. ISSF Technical Report 2017-06.
2017: 1–40.
[6] Perdana A, Batubara A, Nur F, Aprilla R. Population dynamics of sumbo fish Selar crumenophthalmus (Pisces:
Carangidae) in Banda Aceh waters, Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science.
2019: 348. 012016.

International Journal of Life Science Research Archive, 2023, 04(02), 031–038
38
[7] Shewit G, Bruneel S, Getahun A, Anteneh W, Goethals P.Scientific Methods to Understand Fish Population
Dynamics and Support Sustainable Fisheries Management. Water. 2021: 13. 574.
[8] Laplanche C, Elger A, Santoul F, Thiede GP, Budy P. Modeling the fish community population dynamics and
forecasting the eradication success of an exotic fish from an alpine stream. Biological Conservation. 2019: 223.
34-46.
[9] Abdussamad E, Koya K, Rohit P, Joshi KK, Ghosh S, Elayath M, Prakasan D, Sebastine M, Beni M, Syda Rao. Fishery
of yellowfin tuna Thunnus albacares (Bonnaterre, 1788) in the Indian EEZ with special reference to their biology
and population characteristics. Indian Journal of Fisheries. 2012: 59.
[10] Alexander, MA, Khan AC, Mill TS, Mingguo J, Hazmi, Arthur B, Amirul K, Nicholas VCP. Reliability of the data on
tuna catches obtained from the dockside in Indonesia: A study of stakeholders’ perceptions. Marine Policy. 2020:
122, 104242
[11] Ramesh K, Sundaramoorthy B, Neethiselvan N, Athithan S, Rajan Kumar, Shikha Rahangdale. Fishery and length
based population parameters of little tuna, Euthynnus affinis (Cantor, 1849) from Gulf of Mannar, Southwestern
Bay of Bengal. Indian Journal of Geo Marine Sciences. 2019: 48 (11), 1708-1714
[12] Ariadno MK. (2013). Review Of Indonesian Legal Arrangement On Tuna Fisheries. Indonesian Journal of
International Law. 2013: 10 (3).
[13] Nurdin E, Sondita MFA, Yusfiandayani R, Baskoro MS. Growth and mortality parameters of yellowfin tuna
(Thunnus albacares) in Palabuhanratu waters, west Java (eastern Indian Ocean). AACL Bioflux. 2016: 9(3), 741–
747.
[14] Ghofar A, Saputra SW, Sabdono A, Solichin A, Taufani WT, Febrianto S. Population dynamics of Yellowfin Tuna
Thunnus albacares (Bonnaterre, 1788) in the fisheries management area 573 of the Indian Ocean. Croatian
Journal of Fisheries. 2021: 79 (b2).
[15] Farhad K, Seyed AH, Mohammad D. Estimates of Length-Based Population Parameters of Yellowfin Tuna
(Thunnus albacares) in the Oman Sea. Turkish Journal of Fisheries and Aquatic Sciences. 2014: 14: 101-111