Classification models based on the gonadosomatic index to determine gonadal maturity stages: a case study in the Peruvian anchovy Engraulis ringens
DOI:
https://doi.org/10.3989/scimar.05636.117Keywords:
gonadal maturity, gonadosomatic index, Peruvian anchovy, classification models, random forest, machine learningAbstract
Accurate classification of gonadal maturity stages is essential to ensure reliable estimates of reproductive indicators. In Peruvian anchovy (Engraulis ringens), two gonadal maturity scales have been used: the empirical scale proposed by Einarsson et al. (1966) and the histologically validated scale developed by Buitrón et al. (2015). The adoption of this scale underscores the need to harmonize or retrospectively adjust historical maturity records, which is essential for analysing long-term changes in anchovy reproductive dynamics. In this study, we evaluate the efficacy of the gonadosomatic index (GSI) in predicting stages and groups of gonadal maturity. Data from 115 research surveys conducted between 1984 and 2022 were analysed. Classification models using different algorithms were applied, with the random forest algorithm demonstrating the highest accuracy and robustness, achieving a mean accuracy of >90% and a logarithmic loss of 0.13. GSI and total length were identified as the most influential predictors of gonadal maturity. The classification of historical data (1984–2014) using the M-5 model (which includes GSI, total length, latitude, distance from the coast and condition factor) demonstrated high concordance with the reproductive patterns observed in recent periods (2015-2022), supporting its predictive reliability. The results indicate that the combined use of GSI and machine learning-based classification models is a valuable tool for identifying gonadal maturity stages from historical data and harmonizing them with the current maturity scale, facilitating the interpretation of variations in the reproductive behaviour of the resource over time.
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Bishop C.M., Nasrabadi N.M. 2006. Pattern Recognition and Machine Learning. Springer, New York, 738 pp.
Basilone G., Ferreri R., Barra M., et al. 2020. Spawning ecology of the European anchovy (Engraulis encrasicolus) in the Strait of Sicily: Linking variations of zooplankton prey, fish density, growth, and reproduction in an upwelling system. Prog. Oceanogr. 184: 102330. https://doi.org/10.1016/j.pocean.2020.102330
Breiman L., Friedman J., Olshen R.A., et al. 1984. Classification and Regression Trees. Chapman and Hall/CRC, New York, 368 pp.
Breiman L. 2001. Random Forests. Machine Learning 45(1): 5-32. https://doi.org/10.1023/A:1010933404324
Bromley P.J., Casey J. 2001. An attempt to ascertain the spawning fraction of female North Sea whiting based on visual staging of maturity. In: Kjesbu O.S., Hunter J.R., Witthames P.R. Report of the working group on Modern approaches to assess maturity and fecundity of warm- and cold-water fish and squids. Fish. Board Norw., Inst. Mar. Res., Bergen, pp. 25-28.
Brown-Peterson N.J., Leaf R.T., Leontiou A.J. 2021. Importance of depth and artificial structure as predictors of female red snapper reproductive parameters: Red Snapper Reproduction. Trans. Am. Fish. Soc. 150(1): 115-129. https://doi.org/10.1002/tafs.10277
Brownscombe J.W., Griffin L.P., Morley D., et al. 2020. Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources. Oecologia 194(1): 283-298. https://doi.org/10.1007/s00442-020-04753-2 PMid:33006076
Buitrón B., Perea A. 2000. Aspectos reproductivos de la anchoveta peruana durante el periodo 1992-2000. Bol. Inst. Mar Perú 19: 45-53.
Buitrón B., Perea Á., Mori J., et al. 2011. Protocolo para estudios sobre el proceso reproductivo de peces pelágicos y demersales. Inf. Inst. Mar Perú 38(4): 373-383. https://hdl.handle.net/20.500.12958/2172
Buitrón B., Perea Á., Mori J., et al. 2015. Escala de madurez gonadal de anchoveta peruana Engraulis ringens (Jenyns, 1842). Bol. Inst. Mar Perú 30(1-2): 10-19. https://hdl.handle.net/20.500.12958/2936
Castillo R., Peraltilla S., Aliaga A., et al. 2009. Protocolo técnico para la evaluación acústica de las áreas de distribución y abundancia de recursos pelágicos en el mar peruano. Inf. Inst. Mar Perú 36(1-2): 1-23. https://hdl.handle.net/20.500.12958/2001
Chawla N.V., Bowyer K.W., Hal L.O., et al. 2002. SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16: 321-357. https://doi.org/10.1613/jair.953
Cohen J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37-46. https://doi.org/10.1177/001316446002000104
Cuba A., Sánchez J., Mori J., et al. 2020. Anomalías de los índices reproductivos, fracción desovante e índice gonadosomático de anchoveta peruana Engraulis ringens (Jenyns, 1842) del stock norte-centro del Perú en relación a El Niño Costero 2017. The Biologist 17(2): 253-261. https://doi.org/10.24039/rtb2019172371
Cuba A., Sánchez J., Mori J. 2023. Effect in the reproduction of north-center Peruvian anchovy Engraulis ringens (Jenyns, 1842) stock from Peru during La Niña Event 2017-2018 categorized by reproductive index anomalies. The Biologist 21(2): 141-146.
DeVlaming V., Grossman G., Chapman F. 1982. On the use of the gonosomatic index. Comp. Biochem. Physiol. A Physiol. 73(1): 31-39. https://doi.org/10.1016/0300-9629(82)90088-3
Djurovic M., Joksimović A., Pešić A., et al. 2018. Reproductive pattern of the anchovy, Engraulis encrasicolus (Linnaeus, 1758), in the Boka Kotorska Bay (Montenegro, southern Adriatic Sea). Acta Adriat. 59(2): 173-184. https://doi.org/10.32582/aa.59.2.2
Domínguez-Petit R., Anastasopoulou A., Cubillos L., et al. 2017. Maturity. Action COST FRESH (F0601). NAFO Working Group on Reproductive Potential.
Einarsson H., Flores F., Miñano J. 1966. El ciclo de madurez de la anchoveta peruana (Engraulis ringens J.). In: I Seminario Latinoamericano sobre el Océano Pacífico Oriental. Univ. Nacional Mayor, San Marcos, Lima, pp. 128-135.
Engelhard G.H., Dieckmann U., Godø O. R. 2003. Age at maturation predicted from routine scale measurements in Norwegian spring-spawning herring (Clupea harengus) using discriminant and neural network analyses. ICES J. Mar. Sci. 60(2): 304-313. https://doi.org/10.1016/S1054-3139(03)00017-1
Erickson D.L., Hightower J.E., Grossman G.D. 1985. The relative gonadal index: an alternative index for quantification of reproductive condition. Comp. Biochem. Physiol. A 81: 117-120. https://doi.org/10.1016/0300-9629(85)90276-2 PMid:2859944
FAO. 2024. The State of World Fisheries and Aquaculture 2024 - Blue Transformation in action. Rome.
Fernandes J.A., Irigoien X., Goikoetxea N., et al. 2010. Fish recruitment prediction, using robust supervised classification methods. Ecol. Modell. 221(2): 338-352. https://doi.org/10.1016/j.ecolmodel.2009.09.020
Fernandes J.A., Irigoien X., Lozano J.A., et al. 2015. Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species. Ecological Informatics 25: 35-42. https://doi.org/10.1016/j.ecoinf.2014.11.004
Flores A., Wiff R., Díaz E. 2015. Using the gonadosomatic index to estimate the maturity ogive: application to Chilean hake (Merluccius gayi gayi). ICES J. Mar. Sci. 72(2): 508-514. https://doi.org/10.1093/icesjms/fsu155
Flores A., Wiff R., Ganias K., et al. 2019. Accuracy of gonadosomatic index in maturity classification and estimation of maturity ogive. Fish. Res. 210: 50-62. https://doi.org/10.1016/j.fishres.2018.10.009
Flores A., Wiff R., Donovan C.R., et al. 2024. Applying machine learning to predict reproductive condition in fish. Ecological Informatics 80: 102481. https://doi.org/10.1016/j.ecoinf.2024.102481
Fréon P., Bouchon M., Mullon C., et al. 2008. Interdecadal variability of anchoveta abundance and overcapacity of the fishery in Peru. Prog. Oceanogr. 79(2-4): 401-412. https://doi.org/10.1016/j.pocean.2008.10.011
Fukuda N., Aoyama J., Miller M.J., et al. 2013. Discriminant analysis of age to enable rapid estimation of 0-age Japanese eels Anguilla japonica. Coast. Mar. Sci. 36: 19-25.
Gerritsen H.D., McGrath D., Lordan C. 2006. A simple method for comparing age-length keys reveals significant regional differences within a single stock of haddock (Melanogrammus aeglefinus). ICES J. Mar. Sci. 63(6): 1096-1100. https://doi.org/10.1016/j.icesjms.2006.04.008
Heincke F. 1908. Bericht über die Untersuchungen der Biologischen Anstalt auf Helgoland zur Naturgeschichte der Nutzfische. In: Deutsche Wissenschaftliche Kommission für Meeresforschung, Die Beteiligung Deutschlands an der Internationalen Meeresforschung 1908:4/5:67-155.
Hunter J.R., Macewicz B.J. 2003. Improving the accuracy and precision of reproductive information used in fisheries. In: Kjesbu O.S., Hunter J.R., Witthames P.R. (eds), Report of the working group on Modern approaches to assess maturity and fecundity of warm- and cold-water fish and squids. Fish. Board Norw., Inst. Mar. Res., Bergen, pp. 57-68.
Ibaibarriaga L., Bernal M., Motos L., et al. 2007. Characterization of stage-classified biological processes using multinomial models: a case study of anchovy (Engraulis encrasicolus) eggs in the Bay of Biscay. Can. J. Fish. Aquat. Sci. 64(3): 539-553. https://doi.org/10.1139/f07-030
ICES. 2018. Report of the Workshop for Advancing Sexual Maturity Staging in Fish (WKASMSF), 30 April - 4 May 2018, ICES Headquarters, Copenhagen, Denmark. ICES CM/EOSG: 38. 75 pp.
Jamshidi E.J., Yusup Y., Kayode J.S., et al. 2022. Detecting outliers in a univariate time series dataset using unsupervised combined statistical methods: A case study on surface water temperature. Ecological Informatics 69:101672. https://doi.org/10.1016/j.ecoinf.2022.101672
Jordán R. 1980. Biology of the anchoveta. In: Summary of the present knowledge. Proceedings of the Workshop on the Phenomenon known as "El Niño". Guayaquil, Ecuador, 4-12 December 1974, United Nations Educational, Scientific and Cultural Organization, Paris, pp. 249-276.
Kell L., Nash R., Dickey-Collas M., et al. 2016. Is spawning stock biomass a robust proxy for reproductive potential? Fish and Fisheries 17(3): 596-616. https://doi.org/10.1111/faf.12131
Kjesbu O.S., Hunter J.R., Witthames P.R. 2003. Report of the working group on modern approaches to assess maturity and fecundity of warm- and cold-water fish and squids, Fisken og. Havet 12: 117-124.
Kjesbu O. 2016. Applied fish reproductive biology: contribution of individual reproductive potential to recruitment and fisheries management. In: Jakobsen T., Fogarty M., Megrey B., et al. (eds), Fish reproductive biology: implications for assessment and management. John Wiley and Sons, pp. 293-332. https://doi.org/10.1002/9781444312133.ch8
Lemaître G., Nogueira F., Aridas C. K. 2017. Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. 18(17): 1-5.
Liaw A., Wiener M. 2002. Classification and Regression by Random Forest. R News 2: 18-22.
Lowerre-Barbieri S., Ganias K., Saborido-Rey F., et al. 2011. Reproductive timing in marine fishes: variability, temporal scales, and methods. Mar. Coast. Fish. 3(1): 71-91. https://doi.org/10.1080/19425120.2011.556932 PMCid:PMC9121275
McPherson L.R., Ganias K., Marshall C.T. 2011. Inaccuracies in routinely collected Atlantic herring (Clupea harengus) maturity data and correction using a gonadosomatic index model. J. Mar. Biol. Assoc. U.K. 91(7): 1477-1487. https://doi.org/10.1017/S002531541100018X
Mori J., Buitrón B., Perea A., et al. 2011. Interannual variability of the reproductive strategy of the Peruvian anchovy off northern-central Peru. Ciencias Marinas 37(4B): 513-525. https://doi.org/10.7773/cm.v37i4B.1872
Okamura H., Morita S., Kuroda H. 2024. Forecasting fish recruitment using machine learning methods: A case study of arabesque greenling. Fish. Res. 278: 107096. https://doi.org/10.1016/j.fishres.2024.107096
Paluszynska A., Biecek P., Jiang Y., 2022. Explaining and Visualizing Random Forests in Terms of Variable Importance. Available: https://CRAN.R-project.org/package
Peña N., Mendo J., Pellón J.. 1989. Sexual maturity of Peruvian anchoveta (Engraulis ringens), 1961-1987. In: Pauly D, Tsukayama I (eds), The Peruvian Anchoveta and its Upwelling Ecosystem: Three Decades of Change. ICLARM Studies and Reviews 15, pp. 132-142.
Perea Á., Peña C., Oliveros-Ramos R., et al. 2011. Producción potencial de huevos, reclutamiento y veda reproductiva de la anchoveta peruana (Engraulis ringens): Implicaciones en el manejo pesquero. Ciencias marinas 37(4b): 585-601. https://doi.org/10.7773/cm.v37i4B.1827
Perea Á., Buitrón B., Mori J., et al. 2015. Anomalías de los índices reproductivos de anchoveta Engraulis ringens en relación al ambiente. Boletín Trimestral Oceanográfico 1: 25-26.
Potoschi A., Renones O., Cannizzaro L. 1999. Sexual development, maturity and reproduction of dolphinfish (Coryphaena hippurus) in the western and central Mediterranean. Sci. Mar. 63(3-4); 367-372. https://doi.org/10.3989/scimar.1999.63n3-4367
Quinn G.P., Keough M.J. 2002. Experimental design and data analysis for biologists. Cambridge university press, 557 pp. https://doi.org/10.1017/CBO9780511806384
Rahmani A., Kacher M., Iguer-Ouada M. 2020. Oocytes number and surface area as microsopic and quantative parameters to evaluate reproductive activity in Engraulis encrasicolus L. (Clupeiformes: Engraulidae). Cah. Biol. Mar. 61: 271-277.
Saborido-Rey F., Basilone G., Belcari P., et al. 2014. Report of the Workshop for Maturity Staging Chairs (WKMATCH), 11-15 June 2012, Split, Croatia. ICES, 59 pp.
Saetersdal G., Valdivia J. 1964. Un estudio del crecimiento, tamaño y reclutamiento de la anchoveta (Engraulis ringens J) basado en datos de frecuencia de longitudes. Bol. Inst. Invest. Recurs. Mar 1(4): 85-136.
Santander H., Flores R. 1983. Los desoves y distribución larval de cuatro especies pelágicas y sus relaciones con las variaciones del ambiente marino frente al Perú. FAO Fisheries Report (FAO). No. 291(2): 835-870.
Santander H., Alheit J., Smith P.E. 1984. Estimación de la biomasa de la población desovante de anchoveta peruana Engraulis ringens en 1981. Por aplicación del "método de producción de huevos". Bol. Inst. Mar Perú 8: 209-250.
Skjæraasen J.E., Nash R.D.M., Korsbrekke K., et al. 2012. Frequent skipped spawning in the world's largest cod population. Proc. Natl. Acad. Sci. U.S.A. 109(23): 8995-8999. https://doi.org/10.1073/pnas.1200223109 PMid:22615381 PMCid:PMC3384173
Sokolova M., Lapalme G. 2009. A systematic analysis of performance measures for classification tasks. Information processing and management 45(4): 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
Somarakis S., Ganias K., Tsepes G., et al. 2004. Ovarian allometry and the use of gonadosomatic index: a case of study in the Mediterranean sardine, Sardina pilchardus. Mar. Biol. 146: 181-189. https://doi.org/10.1007/s00227-004-1419-2
Takahashi K., Mosquera K., Reupo J. 2014. El Índice Costero El Niño (ICEN): historia y actualización. Boletín técnico: Generación de modelos climáticos para el pronóstico de la ocurrencia del Fenómeno El Niño. Instituto Geofísico del Perú 1(2): 8-9.
Tam J., Blaskovic V., Goya E., et al. 2010. Relación entre anchoveta y otros componentes del ecosistema. Bol. Inst. Mar Perú 25: 95-108.
Taylor M.H., Tam J., Blaskovic V., et al. 2008. Trophic modeling of the Northern Humboldt Current Ecosystem, Part II: Elucidating ecosystem dynamics from 1995 to 2004 with a focus on the impact of ENSO. Prog. Oceanogr. 79: 366-378. https://doi.org/10.1016/j.pocean.2008.10.008
Therneau T., Atkinson B., Ripley B. 2013. Rpart: Recursive Partitioning. R Package Version 4.1-3. http://CRAN.R-project.org/package=rpart
Tsukayama I., Alvarez M.A. 1980. Fluctuations in the spawning stock of anchoveta during the spring reproduction cycles from 1964-1978. In: Sharp G.D. (ed), Workshop on the Effects of Environmental Variation on the Survival of Larval Pelagic Fishes. IOC Workshop Report No. 28. IOC/UNESCO, París, pp. 233-253.
Venables W.N., Ripley B.D. 2002. Modern Applied Statistics with S. Springer, New York, 497 pp. https://doi.org/10.1007/978-0-387-21706-2
Vitale F., Svedäng H., Cardinale M. 2006. Histological analysis invalidates macroscopically determined maturity ogives of the Kattegat cod (Gadus morhua) and suggests new proxies for estimating maturity status of individual fish. ICES J. Mar. Sci. 63(3): 485-492. https://doi.org/10.1016/j.icesjms.2005.09.001 PMCid:PMC9745576
Wyanski D.M., Brown-Peterson N. 2010. 4th Workshop on Gonadal Histology of Fishes. Fish Reproduction and Fisheries (FRESH), COST Action FA0601.
West G. 1990. Methods of assessing ovarian development in fishes: a review. Mar. Freshw. Res. 41(2): 199-222. https://doi.org/10.1071/MF9900199
Wright, P., Rowe S. 2019. Reproduction and spawning. In: Rose G. (ed), Atlantic Cod: A Bio-Ecology. John Wiley and Sons, pp. 87-132. https://doi.org/10.1002/9781119460701.ch3
Yoneda M., Kitano H., Selvaraj S., et al. 2013. Dynamics of gonadosomatic index of fish with indeterminate fecundity between subsequent egg batches: application to Japanese anchovy Engraulis japonicus under captive conditions. Mar. Biol. 160: 2733-2741. https://doi.org/10.1007/s00227-013-2266-9
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