Evaluation of fish density influence on the growth of the spotted rose snapper reared in floating net cages using growth models and non-parametric tests
Article Sidebar
Main Article Content
Abstract
In commercial fish farming, growth performance is the most influential factor in economic profitability; so, biomass optimization has become a growing concern. We analyzed the influence of 3 harvest densities (15, 20, and 22 kg·m–3) on the growth of spotted rose snappers reared in floating net cages during a production cycle. To assess the impact of stocking density on growth performance, we used 2 indicators: final total length-at-age (12 months) and the growth rate estimated from growth models (von Bertalanffy, logistic, and Gompertz). For the first indicator, we tested for normality. We did the Kruskal–Wallis and the post hoc Kruskal–Wallis tests to compare the mean total final length from each density. Accordingly, the means of densities D15 and D20 were the same (P value = 0.22). For the second indicator, we fitted the models with the subroutine optim of the R statistical package using the L-BFGS-B algorithm. Model selection was made with the Akaike and the Bayesian information criteria. Both criteria suggested that the logistic model fitted the data best. With the best model (logistic), we did 1,000 bootstrap simulations for each density scenario to determine the distribution of the maximum likelihood estimation for the instantaneous growth rate. Because the estimates were normally distributed, we used ANOVA to test the equality of the instantaneous growth. The Tukey HSD test suggested that all means were statistically different from each other. The fastest growth rate (K = 0.275) corresponded to the cage with a density of 20 kg·m–3. These findings demonstrate that the logistic model can predict the growth of spotted rose snappers under culture conditions using floating net cages. These results strengthen the productive potential and economic profitability of snapper aquaculture using floating cage and may help the start of commercial scale aquaculture.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
This is an open access article distributed under a Creative Commons Attribution 4.0 License, which allows you to share and adapt the work, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Figures, tables and other elements in the article are included in the article’s CC BY 4.0 license, unless otherwise indicated. The journal title is protected by copyrights and not subject to this license. Full license deed can be viewed here.
Metrics
References
Abdo de la Parra MI, Rodríguez-Ibarra L, Rodríguez-Montes de Oca G, Velasco-Blanco G, Ibarra-Castro L. 2015. Estado actual del cultivo de larvas del pargo flamenco (Lutjanus guttatus) = State of art for larval rearing of spotted rose snapper (Lutjanus guttatus). Lat Am J Aquat Res. 43(3):415–423. http://doi.org/10.3856/vol43-issue3-fulltext-3 DOI: https://doi.org/10.3856/vol43-issue3-fulltext-3
Akaike H. 1992. Information theory and an extension of the maximum likelihood principle. In: Kotz S, Johnson N (eds.), Breakthroughs in Statistics. New York (USA): Springer Verlag. p. 610–624. DOI: https://doi.org/10.1007/978-1-4612-0919-5_38
Álvarez-Lajonchere L, Abdo de la Parra M, Rodríguez-Ibarra L, Velasco-Blanco G, Puello-Cruz AC, González Rodríguez B, Ibarra-Soto A, Ibarra-Castro L. 2012. The scale-up of spotted rose snapper, Lutjanus guttatus, larval rearing at Mazatlan, Mexico. J World Aquac Soc 43:411–422. DOI: https://doi.org/10.1111/j.1749-7345.2012.00573.x
Ansah YB, Frimpong EA. 2015. Using model-based inference to select a predective growth curve for farmed tilapia. N Am J Aquacult. 77(3):281–288. https://doi.org/10.1080/15222055.2015.1020080 DOI: https://doi.org/10.1080/15222055.2015.1020080
Apu JK, Rahman MS, Rashid H. 2012. Effects of fish population densities on growth and production of fishes. Progress Agric. 23(1–2):63–73. https://doi.org/10.3329/pa.v23i1-2.16566 DOI: https://doi.org/10.3329/pa.v23i1-2.16566
Arocha F, Moreno C, Beerkircher K, Lee DW, Marcano L. 2002. Update on growth estimates for swordfish, Xiphias gladius, in the Northwestern Atlantic. Col Vol Sci Pap ICCAT. 55(4):1416–1429.
Arzola-Sotelo E. 2014. Aplicación del enfoque multimodelo para la evaluación del crecimiento individual de la curvina golfina Cynoscion othonopterus en el Alto Golfo de California. Rev Cienc Pesq. 22(1):79–88.
Baer A, Schulz C, Traulsen I, Krieter J. 2011. Analysing the growth of turbot (Psetta maxima) in a commercial recirculation system with the use of 3 different growth models. Aquacult Int. 19:497–511. https://doi.org/10.1007/s10499-010-9365-0 DOI: https://doi.org/10.1007/s10499-010-9365-0
Bremigan MT, Dettmers JM, Mahan AL. 2003. Zooplankton selectivity by larval yellow perch in Green Bay, Lake Michigan. J Great Lakes Res. 29(3):501–510. https://doi.org/10.1016/s0380-1330(03)70454-7 DOI: https://doi.org/10.1016/S0380-1330(03)70454-7
Byrd RH, Lu P, Nocedal J, Zhu C. 1995. A limited memory algorithm for bound constrained optimization. SIAM J Sci Comput. 16(5):1190–1208. https://doi.org/10.1137/0916069 DOI: https://doi.org/10.1137/0916069
Cailliet GM, Smith WD, Mollet HF, Goldman KJ. 2006. Age and growth studies of chondrichthyan fishes: the need for consistency in terminology, verification, validation, and growth function fitting. Environ Biol Fish. 77:211–228. https://doi.org/10.1007/s10641-006-9105-5 DOI: https://doi.org/10.1007/978-1-4020-5570-6_2
Castillo-Vargasmachuca SG, Ponce-Palafox JT, Arámbul-Muñoz E, Rodríguez-Domínguez G, Aragón-Noriega EA. 2018. The spotted rose snapper (Lutjanus guttatus Steindachner 1869) farmed in marine cages: review of growth models. Rev Aquacult. 10(2):376–384. https://doi.org/10.1111/raq.12166 DOI: https://doi.org/10.1111/raq.12166
Costa AAP, Roubach R, Dallago BSL, Bueno GW, McManus C, Bernal FEM. 2017. Influence of stocking density on growth performance and welfare of juvenile tilapia (Oreochromis niloticus) in cages. Arq Bras Med Vet Zootec. 69(1):243–251. http://doi.org/10.1590/1678-4162-8939 DOI: https://doi.org/10.1590/1678-4162-8939
Gompertz B. 1825. On the nature of the function expressive of the law of human mortality and on a new mode determining the value of life contingences. Philos T R SOC A. 115(1825):515–585. DOI: https://doi.org/10.1098/rstl.1825.0026
Gonçalves-de Oliveira E, Banhos-Pinheiro A, Queiroz-de Oliveira V, Melo da Silva Jr AR, Gazzineo-de Moraes M, Branco-Rocha ÍRC, Rocha-de Sousa R, Farias-Costa FH. 2012. Effects of stocking density on the performance of juvenile pirarucu (Arapaima gigas) in cages. Aquaculture. 370–371:96–101. https://doi.org/10.1016/j.aquaculture.2012.09.027 DOI: https://doi.org/10.1016/j.aquaculture.2012.09.027
Handeland SO, Imsland AK, Stefansson SO. 2008. The effect of temperature and fish size on growth, feed intake, food conversion efficiency and stomach evacuation rate of Atlantic salmon post-smolts. Aquaculture. 283(1–4):36–42. https://doi.org/10.1016/j.aquaculture.2008.06.042 DOI: https://doi.org/10.1016/j.aquaculture.2008.06.042
Hernández C, Ibarra-Castro L, Hernández CH, Quintero-Martínez G, Aragón-Noriega EA, Tacon AG. 2015. Growth performance of spotted rose snapper in floating cages and continuous waterflow tank systems. N Am J Aquacult. 77(4): 423–428. https://doi.org/10.1080/15222055.2015.1032458 DOI: https://doi.org/10.1080/15222055.2015.1032458
Hernández CH, Hernández C, Martínez-Cordero FJ, Castañeda- Lomas N, Rodríguez-Dominguez G, Tacon AGJ, Aragón- Noriega EA. 2016. Effect of Density at harvest on the growth performance and profitability of hatchery-reared spotted rose snapper, Lutjanus guttatus, cultured in floating net cages. J World Aquacult Soc. 47(1):51–60. https://doi.org/10.1111/jwas.12253 DOI: https://doi.org/10.1111/jwas.12253
Hilborn R, Mangel M. 1997. The ecological detective: Confronting models with data. Monographs in population biology 28. Princeton (NJ): Academic Press. 315 p. https://doi.org/10.1515/9781400847310 DOI: https://doi.org/10.1515/9781400847310
Ibarra-Castro L, Navarro-Flores J, Sánchez-Télles JL, Martínez- Brown JM, Ochoa-Bojorquez LA, Rojo-Cebreros AH. 2017. Hatchery production of Pacific white snook at CIAD-Unity Mazatlan, Mexico. World Aquac. 48(3):25–29.
Islam GMN, Tai SY, Kusairi MN. 2016. A stochastic frontier analysis of technical efficiency of fish cage culture in Peninsular Malaysia. SpringerPlus. 5(1):1127. https://doi.org/10.1186/s40064-016-27775-3 DOI: https://doi.org/10.1186/s40064-016-2775-3
Jessop BM. 2010. Geographic effects on American eel (Anguilla rostrata) life history characteristics and strategies. Can J Fish Aquat Sci. 67(2):326–346. https://doi.org/10.1139/f09-189 DOI: https://doi.org/10.1139/F09-189
Jurado-Molina J, Gutiérrez-Benítez O, Roldan-Heredia A. 2018. Model uncertainty and Bayesian estimation of growth parameters of Yellowtail Snapper (Ocyurus chrysurus) form Veracruz, Mexico. Hidrobiológica. 28(2):191–199. https://doi.org/10.24275/uam/izt/dcbs/hidro/2018v28n2/jurado DOI: https://doi.org/10.24275/uam/izt/dcbs/hidro/2018v28n2/Jurado
Katsanevakis S. 2006. Modelling fish growth: Model selection, multi-model inference and model selection uncertainty. Fish Res. 81(2–3):229–235. https://doi.org/10.1016/j.fishres.2006.07.002 DOI: https://doi.org/10.1016/j.fishres.2006.07.002
Katsanevakis S, Maravelias CD. 2008. Modelling fish growth: multi-model inference as a better alternative to a priori using von Bertalanffy equation. Fish Fish. 9(2):178–187. https://doi.org/10.1111/j.1467-2979.2008.00279.x DOI: https://doi.org/10.1111/j.1467-2979.2008.00279.x
Katzenmeyer ED. 2010. Fish growth responses to a changing environment: effects of aquatic nuisance species and environmental conditions in a shallow, eutrophic lake [MSc thesis]. [Ames (Iowa)]: Iowa State University. Department of Natural Resource and Management. 99 p. https://doi.org/10.31274/etd-180810-1635 DOI: https://doi.org/10.31274/etd-180810-1635
Kimura DK. 1980. Likelihood methods for the von Bertalanffy growth curve. Fish Bull. 77:765–774.
Lambert Y, Dutil JD. 2001. Food intake and growth of adult Atlantic cod (Gadus morhua L.) reared under different conditions of stocking density, feeding frequency and size-grading. Aquaculture. 192(2–4):233–247. https://doi.org/10.1016/S0044-8486(00)00448-8S DOI: https://doi.org/10.1016/S0044-8486(00)00448-8
Long L, Zhang H, Ni Q, Liu H, Wu F, Wang X. 2019. Effects of stocking density on growth, stress, and immune responses of juvenile Chinese sturgeon (Acipenser sinensis) in a recirculating aquaculture system. Comp Biochem Physiol, Part C: Toxicol Pharmacol. (219):25–34. https://doi.org/10.1016.j.cbpc.2019.02.002 DOI: https://doi.org/10.1016/j.cbpc.2019.02.002
Lorenzen K, Enberg K. 2002. Density dependent growth as key mechanism in the regulation of fish populations: evidence from among-population comparisons. P Roy Soc B. 269(1486):49–54. https://doi.org/10.1098/rspb.2001.1853 DOI: https://doi.org/10.1098/rspb.2001.1853
Martínez-Brown JM, Ibarra-Castro L, Rojo-Cebreros AH, López- Cabanillas J, Rodríguez-Trejo M, Ortíz-Galindo J. 2018. Acute hyperosmotic stress test for vigor assessment of fisrt-feeding larvae of spotted sand bass Paralabrax maculatofosciatus and spotted rose snapper Lutjanus guttatus = Prueba de estrés hiperosmótico agudo para evaluar el vigor de larvas a la primera alimentación de la cabrilla arenera Paralabrax maculatofasciatus y del pargo flamenco Lutjanus guttatus. Rev Biol Mar Oceanogr. 53(1):87–93. https://doi.org/10.4067/s0718-19572018000100087 DOI: https://doi.org/10.4067/S0718-19572018000100087
Martínez-Cordero FJ, Sánchez-Zazueta E, Hernández C. 2017. Investment analysis of marine cage culture by applying bioeconomic reference points: A case study of the spotted rose snapper (Lutjanus guttatus) in Mexico. Aquacult Econ Manage. 22(2):209–228. https://doi.org/10.1080/13657305.2017.1295489 DOI: https://doi.org/10.1080/13657305.2017.1295489
M’balaka M, Kassam D, Rusuwa B. 2012. The effect of stocking density on the growth and survival of improved and unimproved strains of Oreochromis shiranus. Egypt J Aquat Res. 38(3):205–211. https://doi.org/10.1016/j.ejar.2012.12.013 DOI: https://doi.org/10.1016/j.ejar.2012.12.013
Noble C, Kadri S, Mitchel DF, Huntingford FA. 2007. Influence of feeding regime on intraspecific competition, fin damage and growth in 1+ Atlantic salmon parr (Salmo salar L.) held in freshwater production cages. Aquacult Res. 38(11):1137–1143. http://doi.org/10.1111/j.1365-2109.2007.01777.x DOI: https://doi.org/10.1111/j.1365-2109.2007.01777.x
Papoutsoglou SE, Tziha G, Vrettos X, Athanasiou A. 1998. Effects of stocking density on behavior and growth rate of European sea bass (Dicentrarchus labrax) juveniles reared in a closed circulated system. Aquac Eng. 18(2):135–144. https://doi.org/10.1016/s0144-8609(98)00027-2 DOI: https://doi.org/10.1016/S0144-8609(98)00027-2
Pörtner HO, Berdal B, Blust R, Brix O, Colosimo A, Wachter B De, Guiliani A, Johansen T, Fisher T, Knust R, et al. 2001. Climate induced temperature effects on growth performance, fecundity and recruitment in marine fish: developing a hypothesis for cause and effect relationships in Atlantic cod (Gadus morhua) and common eelpout (Zoarces viviparus). Cont Shelf Res. 21(18–19):1975–1997. DOI: https://doi.org/10.1016/S0278-4343(01)00038-3
Quinn TJ, Deriso RB. 1999. Quantitative fish dynamics. 1st ed. New York (USA): Oxford University Press. 543 p. DOI: https://doi.org/10.1093/oso/9780195076318.003.0001
R Core Team. 2020. R: A language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing; [accessed 2020 February 17]. https://www.r-project. org/
Ricker WE. 1975. Computation and interpretation of biological statistic of fish populations. Fish Res Board Can Bulletin. 191.
Rieman BE, Myers DL. 1992. Influence of fish density and relative productivity on growth of kokanee in ten oligotrophic lakes and reservoirs in Idaho. Trans Am Fish Soc. 121(2):178–191. https://doi.org/10.1577/1548-8659(1992)121<0178:iofdar>2.3.co;2 DOI: https://doi.org/10.1577/1548-8659(1992)121<0178:IOFDAR>2.3.CO;2
Saoud IP, Ghanawi J, Lebbos N. 2008. Effects of stocking density on the survival, growth, size variation and condition index of juvenile rabbitfish Siganus rivulatus. Aquacult Int. 16:109–116. https://doi.org/10.1007/s10499-007-9129-7 DOI: https://doi.org/10.1007/s10499-007-9129-7
Schnute J. 1981. A versatile growth model with statistically stable parameters. Can J Fish Aquat Sci. 38(9):1128–1140. https://doi.org/10.1139/f81-153 DOI: https://doi.org/10.1139/f81-153
Schnute JT, Richards LJ. 1990. A unified approach to the analysis of fish growth, maturity, and survivorship data. Can J Fish Aquat Sci. 47(1):24–40. https://doi.org/10.1139/f90-003 DOI: https://doi.org/10.1139/f90-003
Schwarz GE. 1978. Estimating the dimension of a model. Ann Stat. 6(2):461–464. https://doi.org/10.1214/aos/1176344136 DOI: https://doi.org/10.1214/aos/1176344136
[SEPESCA] Secretaría de Pesca y Acuacultura, Subsecretaría de Fomento y Desarrollo Pesquero, Instituto de Acuacultura del Estado de Sonora. 1994. Desarrollo Científico y Tecnológico para el Cultivo de Snapper (Lutjanus sp) en Jaulas Flotantes Mexico: SEPESCA. 86 p.
Silva-Carrillo Y, Hernández C, Hardy WR, González-Rodríguez B, Castillo-Vargasmachuca S. 2012. The effect of substituting fish meal with soybean meal on growth, feed efficiency, body composition and blood chemistry in juvenile spotted rose snapper, Lutjanus guttatus (Steindachner, 1869). Aquaculture. 364–365:180–185. https://doi.org/10.1016/j.aquaculture.2012.08.007 DOI: https://doi.org/10.1016/j.aquaculture.2012.08.007
Staggs MD, Otis KJ. 1996. Factors affecting first-year growth of fishes in Lake Winnebago, Wisconsin. N Am J Fish Manage. 16(3):608–618. https://doi.org/10.1577/1548-8675(1996)016<0608:fafygo>2.3.co;2 DOI: https://doi.org/10.1577/1548-8675(1996)016<0608:FAFYGO>2.3.CO;2
Von Bertalanffy L. 1957. Quantitative laws in metabolism and growth. Q Rev Biol. 32(3):217–231. https://doi.org/10.1086/401873 DOI: https://doi.org/10.1086/401873
Yoshioka H, Yaegashi Y, Yoshioka Y, Tsugihashi K. 2019. A short note on analysis and application of a stochastic open-ended logistic growth model. Letters in Biomathematics. 6(1):67–77. https://doi.org/10.1080/23737867.2019.1691946 DOI: https://doi.org/10.30707/LiB6.1Yoshioka