Developing a size indicator for fish populations

Monitoring temporal and/or spatial variations in fish size-at-age data can often provide fisheries managers with important information about the status of fish stocks and therefore help them identify necessary changes in management policies. However, due to the multivariate nature of size-at-age dat...

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Published in:Scientia Marina
Main Authors: Chen, Yong, Chen, Xinjun, Xu, Liuxiong
Format: Article in Journal/Newspaper
Language:English
Published: Consejo Superior de Investigaciones Científicas 2008
Subjects:
Online Access:https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819
https://doi.org/10.3989/scimar.2008.72n2221
id ftjscientiamarin:oai:scientiamarina.revistas.csic.es:article/819
record_format openpolar
institution Open Polar
collection Scientia Marina (E-Journal)
op_collection_id ftjscientiamarin
language English
topic size-at-age
robust
principal component analysis
minimum volume ellipsoid analysis
size indicator
talla-por-edad
robusto
análisis de componentes principales
análisis de elipsoide de volumen mínimo
indicador de talla
spellingShingle size-at-age
robust
principal component analysis
minimum volume ellipsoid analysis
size indicator
talla-por-edad
robusto
análisis de componentes principales
análisis de elipsoide de volumen mínimo
indicador de talla
Chen, Yong
Chen, Xinjun
Xu, Liuxiong
Developing a size indicator for fish populations
topic_facet size-at-age
robust
principal component analysis
minimum volume ellipsoid analysis
size indicator
talla-por-edad
robusto
análisis de componentes principales
análisis de elipsoide de volumen mínimo
indicador de talla
description Monitoring temporal and/or spatial variations in fish size-at-age data can often provide fisheries managers with important information about the status of fish stocks and therefore help them identify necessary changes in management policies. However, due to the multivariate nature of size-at-age data, commonly used single-age-based approaches ignore covariance between sizes of different age groups. Different results may therefore be derived when evaluating temporal variations using different age groups for the comparison. The possibility of atypical errors in size-at-age data due to ageing and measurement errors further complicates the comparison. We propose a two-step approach for developing an indicator for monitoring temporal and/or spatial variation in size-at-age data. A robust approach, minimum volume ellipsoid analysis, is used to identify possible outliers in size-at-age data. Then a weighted principal component analysis is applied to the data with the identified outliers down-weighted. An indicator is defined from the resultant principal components for monitoring temporal/spatial variations in size-at-age data. We illustrate the proposed approach with size-at-age data for cod (Gadus morhua) in the northwest Atlantic, NAFO subdivision 3Ps. The overall size-at-age indicator identified shows that the pre-1980 year classes tend to have a much higher size-at-age than the post-1980 year classes. El seguimiento de las variaciones temporales y/o espaciales de datos de talla por edad en peces puede, a menudo, aportar información a los gestores de pesquerías sobre el estado de explotación de los stocks de peces y ayudarles a identificar los cambios necesarios en políticas de gestión. Sin embargo, debido a la naturaleza multivariante de los datos de talla por edad, las aproximaciones tradicionalmente empleadas, basadas en el análisis de una sola clase de edad, ignoran la covarianza entre tallas de distintos grupos de edad, lo que puede generar distintos resultados cuando se analizan variaciones temporales mediante la comparación de distintos grupos de edad. La posible existencia de errores atípicos en datos de talla por edad, debidos a errores de atribución de edad o errores de medida, puede complicar más la comparación. Proponemos una aproximación en dos etapas para el desarrollo de un indicador para el seguimiento de variaciones temporales o espaciales en datos de talla por edad. Una aproximación robusta, conocida como análisis de elipsoide de volumen mínimo, nos permite identificar los posibles valores aberrantes en datos de talla por edad, y a continuación aplicamos el análisis ponderado de componentes principales a los datos con los valores aberrantes debidamente ponderados. Los componentes principales resultantes permiten definir el indicador para el seguimiento de las variaciones espacio-temporales en datos de talla por edad. Ilustramos la aproximación propuesta con datos por edad de bacalao (Gadus morhua) in el Atlántico noroccidental, correspondiente a la subdivisión 3Ps de la NAFO. El indicador general de talla por edad obtenido muestra que las clases de edad anteriores a 1980 tienden a tener una talla por edad mucho mayor que las clases de edad posteriores a 1980.
format Article in Journal/Newspaper
author Chen, Yong
Chen, Xinjun
Xu, Liuxiong
author_facet Chen, Yong
Chen, Xinjun
Xu, Liuxiong
author_sort Chen, Yong
title Developing a size indicator for fish populations
title_short Developing a size indicator for fish populations
title_full Developing a size indicator for fish populations
title_fullStr Developing a size indicator for fish populations
title_full_unstemmed Developing a size indicator for fish populations
title_sort developing a size indicator for fish populations
publisher Consejo Superior de Investigaciones Científicas
publishDate 2008
url https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819
https://doi.org/10.3989/scimar.2008.72n2221
long_lat ENVELOPE(9.806,9.806,63.198,63.198)
geographic Sola
geographic_facet Sola
genre Gadus morhua
Northwest Atlantic
genre_facet Gadus morhua
Northwest Atlantic
op_source Scientia Marina; Vol. 72 No. 2 (2008); 221-229
Scientia Marina; Vol. 72 Núm. 2 (2008); 221-229
1886-8134
0214-8358
10.3989/scimar.2008.72n2
op_relation https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819/860
Beacham, T.D. – 1983. Growth and maturity of Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. Tech. Rep Fish. Aquat. Sci., 1142.
Beverton, R.J.H. and S.J. Holt. – 1957. On the dynamics of exploited fish populations. Fish. Invest. Ser. 2 Mar. Fish. G.B. Minist. Agric. Fish. Food ., 19.
Campbell, N.A. – 1980. Robust procedures in multivariate analysis I: robust covariance estimation. Applied Stats., 29: 231-237. doi:10.2307/2346896
Charnov, E. – 1993. Life History Invariants. Oxford University Press, New York.
Chen, Y. and H.H. Harvey. – 1994. Maturation of white sucker, Catostomus commersoni, populations in Ontario. Can. J. Fish. Aquat. Sci., 51: 2066-2076.
Chen, Y. and H.H. Harvey. – 1995. Growth, abundance, and food supply of white sucker. Trans Am. Fish. Soc., 124: 262-271. doi:10.1577/1548-8659(1995)124<0262:GAAFSO>2.3.CO;2
Chen, Y. and G.S. Mello. – 1999. Growth and maturation of cod (Gadus morhua) of different year classes in NAFO Subdivision 3Ps in Northwest Atlantic. Fish. Res., 42: 87-101. doi:10.1016/S0165-7836(99)00036-3
Chen, Y. and J.E. Paloheimo. – 1998. Can a more realistic model error structure improve the parameter estimation in modelling the dynamics of fish populations? Fish. Res., 38: 9-17. doi:10.1016/S0165-7836(98)00115-5
Chen, Y., D.A. Jackson and J.E. Paloheimo. – 1994. Robust regression approach to analyzing fisheries data. Can. J. Fish. Aquat. Sci., 51: 1420-1429.
Cooley, W.W. and P.R. Lohnes. – 1971. Multivariate Data Analysis. John Wiley and Sons, New York.
Devlin, S.J., R. Gnanadesikan and J.R. Kettenring. – 1981. Robust estimation of dispersion matrices and principal components. J. Am. Stat. Ass., 76: 354-362. doi:10.2307/2287836
Gomes, M.C., R.L. Haedrich and M.G. Villagarcia. – 1995. Spatial and temporal changes in the groundfish assemblages on the northeast Newfoundland/Labrador Shelf, Northwest Atlantic, 1978-1991. Fish. Oceanogr., 4: 85-101. doi:10.1111/j.1365-2419.1995.tb00065.x
Hanson, J.M. and G.A. Chouinard. – 1992. Evidence that size-selective mortality affects growth of Atlantic cod (Gadus morhua L.) in the southern Gulf of St. Lawrence. J. Fish. Biol., 41: 31-41. doi:10.1111/j.1095-8649.1992.tb03168.x
Hilborn, R. and C.J. Walters. – 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York.
Hill, R.C., T.H. Fomby and H.H. Johnson. – 1977. Component selection norms for principal component regression. Commun. Stats. Theor. Methods, 6: 309-334. doi:10.1080/03610927708827494
Hutchings, J.A. and R.A. Myers. – 1994. Timing of cod reproduction: interannual variability and the influence of temperature. Mar. Ecol. Prog. Ser., 108: 21-31. doi:10.3354/meps108021
Jackson, D.A. – 1993. Stopping rules in principal component analysis: a comparison of heuristical and statistical approaches. Ecol., 74: 2204-2214. doi:10.2307/1939574
Jackson, D.A. and Y. Chen. – 2003. Robust principal component analysis of ecological data. Environmetrics, 14: 1-11.
Jensen, A.L. – 1996. Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival. Can. J. Fish. Aquat. Sci., 53: 820-822. doi:10.1139/cjfas-53-4-820
Kovtsova, M.V. – 1995. Changes in growth and maturation of the Barents Sea plaice (Pleuronectes platessa L.) in 70s-90s. ICES Council Meeting Papers 12. ICES, Copenhagen (Denmark).
Krohn, M., S. Reidy and S. Kerr. – 1997 Bioenergetic analysis of the effects of temperature and prey availability on growth and condition of northern cod (Gadus morhua). Can. J. Fish. Aquat. Sci., 54 (Suppl. 1): 113-121. doi:10.1139/cjfas-54-S1-113
Krzanowski, W.J. – 1988. Principal of Multivariate Analysis. Clarendon Press, Oxford, UK
Lilly, G.R. – 1996. Growth and condition of cod in Subdivision 3Ps as determined from trawl surveys (1972-1996) and sentinel surveys (1995). DFO Atlantic Fish. Res. Doc., 96/69.
Manly, B.F.J. – 1991. Randomization and Monte Carlo Methods in Biology. Chapman and Hall, New York.
Mason, R.L. and R.F. Gunst. – 1985. Selecting principal components in regression. Stat. Prob. Lett., 3: 299-301. doi:10.1016/0167-7152(85)90059-8
Moreau, J. – 1987. Age and growth of fish. In: R.C. Summerfelt and G.E. Hall [eds.], Fish Growth, pp. 101-143. Iowa State University Press, Ames, Iowa.
Myers, R.A., G. Mertz and P.S. Fowlow. – 1997. Maximum population growth rates and recovery times for Atlantic cod, Gadus morhua. Fish. Bull., 95: 762-772.
Nikolskii, G.V. – 1965. Theory of Fish Population Dynamics. Oliver and Boyd, Edinburgh, U.K.
Paloheimo, J.E. and L.M. Dickie. – 1965. Food and growth of fishes. I. A growth curve derived from experimental data. J. Fish. Res. Bd. Can., 22: 521-542.
Rao, C.R. – 1964. The use and interpretation of principal component analysis in applied research. Sankhya A, 26: 329-358.
Ricker, W.E. – 1975. Computation and interpretation of biological statistics of fish populations. Fish. Res. Board Can., 191.
Roff, D.A. – 1984. The evolution of life history parameters in teleosts. Can. J. Fish. Aquat. Sci., 41: 989-1000.
Rollet, C., J-C. Brêthes and A. Fréchet. – 1995. Spatial and temporal variation in cod length frequencies and length at 50% maturity in Divisions 3Pn, 4RS and 3Ps. DFO Atl. Fish. Res. Doc., 95.
Rousseeuw, P.J. – 1984. Least median of squares regression. J. Am. Stat. Ass., 79: 871-880. doi:10.2307/2288718
Rousseeuw, P.J. – 1985. Multivariate estimation with high breakdown point. In: W. Grossmann, G. Pflug, I. Vincze and W. Wertz (eds.), Mathematical Statistics and Applications (Vol. B), pp. 283-297. Reidel Publishing, Dordrecht, The Netherlands.
Rousseeuw, P.J. and A.M. Leroy. – 1987. Robust Regression and Outlier Detection. John Wiley and Sons Inc. New York.
Rousseeuw, P.J. and B.C. van Zomeren. – 1990. Unmasking multivariate outliers and leverage points. J. Am. Stat. Ass., 85: 633-644. doi:10.2307/2289995
SAS. – 1987. SAS Guide for personal computers. SAS Institute, Cary, North Carolina.
Shelton, P.A., D.E. Stansbury, E.F. Murphy, J. Brattey and G.R. Lilly. – 1996. An assessment of the cod stock in NAFO subdivision 3Ps. DFO Atl. Fish. Res. Doc., 96/91.
Swain, D.P. – 1993. Age- and density-dependent bathymetric pattern of Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci., 50: 1255-1264.
Trippel, E.A. – 1995. Age at maturity as stress indicator in fisheries. BioSci., 45: 759-771. doi:10.2307/1312628
Trippel, E.A. – 1998. Egg size and variability and seasonal offspring production of young Atlantic cod. Trans. Am. Fish. Soc., 127: 339-359. doi:10.1577/1548-8659(1998)127<0339:ESAVAS>2.0.CO;2
Vogt, N.B. and K. Kolsett. – 1987. Composition activity relationships _CARE. Part III. Polynomial principal component regression and response surface analysis of mutagenicity in air samples, 1981. Report 87: 747, Chemometrics and Indelligent Lab Systems.
https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819
doi:10.3989/scimar.2008.72n2221
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spelling ftjscientiamarin:oai:scientiamarina.revistas.csic.es:article/819 2023-05-15T16:19:17+02:00 Developing a size indicator for fish populations Desarrollo de un indicador de talla para poblaciones de peces Chen, Yong Chen, Xinjun Xu, Liuxiong 2008-06-30 application/pdf https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819 https://doi.org/10.3989/scimar.2008.72n2221 eng eng Consejo Superior de Investigaciones Científicas https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819/860 Beacham, T.D. – 1983. Growth and maturity of Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. Tech. Rep Fish. Aquat. Sci., 1142. Beverton, R.J.H. and S.J. Holt. – 1957. On the dynamics of exploited fish populations. Fish. Invest. Ser. 2 Mar. Fish. G.B. Minist. Agric. Fish. Food ., 19. Campbell, N.A. – 1980. Robust procedures in multivariate analysis I: robust covariance estimation. Applied Stats., 29: 231-237. doi:10.2307/2346896 Charnov, E. – 1993. Life History Invariants. Oxford University Press, New York. Chen, Y. and H.H. Harvey. – 1994. Maturation of white sucker, Catostomus commersoni, populations in Ontario. Can. J. Fish. Aquat. Sci., 51: 2066-2076. Chen, Y. and H.H. Harvey. – 1995. Growth, abundance, and food supply of white sucker. Trans Am. Fish. Soc., 124: 262-271. doi:10.1577/1548-8659(1995)124<0262:GAAFSO>2.3.CO;2 Chen, Y. and G.S. Mello. – 1999. Growth and maturation of cod (Gadus morhua) of different year classes in NAFO Subdivision 3Ps in Northwest Atlantic. Fish. Res., 42: 87-101. doi:10.1016/S0165-7836(99)00036-3 Chen, Y. and J.E. Paloheimo. – 1998. Can a more realistic model error structure improve the parameter estimation in modelling the dynamics of fish populations? Fish. Res., 38: 9-17. doi:10.1016/S0165-7836(98)00115-5 Chen, Y., D.A. Jackson and J.E. Paloheimo. – 1994. Robust regression approach to analyzing fisheries data. Can. J. Fish. Aquat. Sci., 51: 1420-1429. Cooley, W.W. and P.R. Lohnes. – 1971. Multivariate Data Analysis. John Wiley and Sons, New York. Devlin, S.J., R. Gnanadesikan and J.R. Kettenring. – 1981. Robust estimation of dispersion matrices and principal components. J. Am. Stat. Ass., 76: 354-362. doi:10.2307/2287836 Gomes, M.C., R.L. Haedrich and M.G. Villagarcia. – 1995. Spatial and temporal changes in the groundfish assemblages on the northeast Newfoundland/Labrador Shelf, Northwest Atlantic, 1978-1991. Fish. Oceanogr., 4: 85-101. doi:10.1111/j.1365-2419.1995.tb00065.x Hanson, J.M. and G.A. Chouinard. – 1992. Evidence that size-selective mortality affects growth of Atlantic cod (Gadus morhua L.) in the southern Gulf of St. Lawrence. J. Fish. Biol., 41: 31-41. doi:10.1111/j.1095-8649.1992.tb03168.x Hilborn, R. and C.J. Walters. – 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. Chapman and Hall, New York. Hill, R.C., T.H. Fomby and H.H. Johnson. – 1977. Component selection norms for principal component regression. Commun. Stats. Theor. Methods, 6: 309-334. doi:10.1080/03610927708827494 Hutchings, J.A. and R.A. Myers. – 1994. Timing of cod reproduction: interannual variability and the influence of temperature. Mar. Ecol. Prog. Ser., 108: 21-31. doi:10.3354/meps108021 Jackson, D.A. – 1993. Stopping rules in principal component analysis: a comparison of heuristical and statistical approaches. Ecol., 74: 2204-2214. doi:10.2307/1939574 Jackson, D.A. and Y. Chen. – 2003. Robust principal component analysis of ecological data. Environmetrics, 14: 1-11. Jensen, A.L. – 1996. Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival. Can. J. Fish. Aquat. Sci., 53: 820-822. doi:10.1139/cjfas-53-4-820 Kovtsova, M.V. – 1995. Changes in growth and maturation of the Barents Sea plaice (Pleuronectes platessa L.) in 70s-90s. ICES Council Meeting Papers 12. ICES, Copenhagen (Denmark). Krohn, M., S. Reidy and S. Kerr. – 1997 Bioenergetic analysis of the effects of temperature and prey availability on growth and condition of northern cod (Gadus morhua). Can. J. Fish. Aquat. Sci., 54 (Suppl. 1): 113-121. doi:10.1139/cjfas-54-S1-113 Krzanowski, W.J. – 1988. Principal of Multivariate Analysis. Clarendon Press, Oxford, UK Lilly, G.R. – 1996. Growth and condition of cod in Subdivision 3Ps as determined from trawl surveys (1972-1996) and sentinel surveys (1995). DFO Atlantic Fish. Res. Doc., 96/69. Manly, B.F.J. – 1991. Randomization and Monte Carlo Methods in Biology. Chapman and Hall, New York. Mason, R.L. and R.F. Gunst. – 1985. Selecting principal components in regression. Stat. Prob. Lett., 3: 299-301. doi:10.1016/0167-7152(85)90059-8 Moreau, J. – 1987. Age and growth of fish. In: R.C. Summerfelt and G.E. Hall [eds.], Fish Growth, pp. 101-143. Iowa State University Press, Ames, Iowa. Myers, R.A., G. Mertz and P.S. Fowlow. – 1997. Maximum population growth rates and recovery times for Atlantic cod, Gadus morhua. Fish. Bull., 95: 762-772. Nikolskii, G.V. – 1965. Theory of Fish Population Dynamics. Oliver and Boyd, Edinburgh, U.K. Paloheimo, J.E. and L.M. Dickie. – 1965. Food and growth of fishes. I. A growth curve derived from experimental data. J. Fish. Res. Bd. Can., 22: 521-542. Rao, C.R. – 1964. The use and interpretation of principal component analysis in applied research. Sankhya A, 26: 329-358. Ricker, W.E. – 1975. Computation and interpretation of biological statistics of fish populations. Fish. Res. Board Can., 191. Roff, D.A. – 1984. The evolution of life history parameters in teleosts. Can. J. Fish. Aquat. Sci., 41: 989-1000. Rollet, C., J-C. Brêthes and A. Fréchet. – 1995. Spatial and temporal variation in cod length frequencies and length at 50% maturity in Divisions 3Pn, 4RS and 3Ps. DFO Atl. Fish. Res. Doc., 95. Rousseeuw, P.J. – 1984. Least median of squares regression. J. Am. Stat. Ass., 79: 871-880. doi:10.2307/2288718 Rousseeuw, P.J. – 1985. Multivariate estimation with high breakdown point. In: W. Grossmann, G. Pflug, I. Vincze and W. Wertz (eds.), Mathematical Statistics and Applications (Vol. B), pp. 283-297. Reidel Publishing, Dordrecht, The Netherlands. Rousseeuw, P.J. and A.M. Leroy. – 1987. Robust Regression and Outlier Detection. John Wiley and Sons Inc. New York. Rousseeuw, P.J. and B.C. van Zomeren. – 1990. Unmasking multivariate outliers and leverage points. J. Am. Stat. Ass., 85: 633-644. doi:10.2307/2289995 SAS. – 1987. SAS Guide for personal computers. SAS Institute, Cary, North Carolina. Shelton, P.A., D.E. Stansbury, E.F. Murphy, J. Brattey and G.R. Lilly. – 1996. An assessment of the cod stock in NAFO subdivision 3Ps. DFO Atl. Fish. Res. Doc., 96/91. Swain, D.P. – 1993. Age- and density-dependent bathymetric pattern of Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci., 50: 1255-1264. Trippel, E.A. – 1995. Age at maturity as stress indicator in fisheries. BioSci., 45: 759-771. doi:10.2307/1312628 Trippel, E.A. – 1998. Egg size and variability and seasonal offspring production of young Atlantic cod. Trans. Am. Fish. Soc., 127: 339-359. doi:10.1577/1548-8659(1998)127<0339:ESAVAS>2.0.CO;2 Vogt, N.B. and K. Kolsett. – 1987. Composition activity relationships _CARE. Part III. Polynomial principal component regression and response surface analysis of mutagenicity in air samples, 1981. Report 87: 747, Chemometrics and Indelligent Lab Systems. https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/819 doi:10.3989/scimar.2008.72n2221 Copyright (c) 2008 Consejo Superior de Investigaciones Científicas (CSIC) https://creativecommons.org/licenses/by/4.0 CC-BY Scientia Marina; Vol. 72 No. 2 (2008); 221-229 Scientia Marina; Vol. 72 Núm. 2 (2008); 221-229 1886-8134 0214-8358 10.3989/scimar.2008.72n2 size-at-age robust principal component analysis minimum volume ellipsoid analysis size indicator talla-por-edad robusto análisis de componentes principales análisis de elipsoide de volumen mínimo indicador de talla info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed article Artículo revisado por pares 2008 ftjscientiamarin https://doi.org/10.3989/scimar.2008.72n2221 https://doi.org/10.3989/scimar.2008.72n2 https://doi.org/10.2307/2346896 https://doi.org/10.1577/1548-8659(1995)124<0262:GAAFSO>2.3.CO;2 https://doi.org/10.1016/S0165-7836(99)00036-3 https://doi.org/10 2022-03-20T16:30:49Z Monitoring temporal and/or spatial variations in fish size-at-age data can often provide fisheries managers with important information about the status of fish stocks and therefore help them identify necessary changes in management policies. However, due to the multivariate nature of size-at-age data, commonly used single-age-based approaches ignore covariance between sizes of different age groups. Different results may therefore be derived when evaluating temporal variations using different age groups for the comparison. The possibility of atypical errors in size-at-age data due to ageing and measurement errors further complicates the comparison. We propose a two-step approach for developing an indicator for monitoring temporal and/or spatial variation in size-at-age data. A robust approach, minimum volume ellipsoid analysis, is used to identify possible outliers in size-at-age data. Then a weighted principal component analysis is applied to the data with the identified outliers down-weighted. An indicator is defined from the resultant principal components for monitoring temporal/spatial variations in size-at-age data. We illustrate the proposed approach with size-at-age data for cod (Gadus morhua) in the northwest Atlantic, NAFO subdivision 3Ps. The overall size-at-age indicator identified shows that the pre-1980 year classes tend to have a much higher size-at-age than the post-1980 year classes. El seguimiento de las variaciones temporales y/o espaciales de datos de talla por edad en peces puede, a menudo, aportar información a los gestores de pesquerías sobre el estado de explotación de los stocks de peces y ayudarles a identificar los cambios necesarios en políticas de gestión. Sin embargo, debido a la naturaleza multivariante de los datos de talla por edad, las aproximaciones tradicionalmente empleadas, basadas en el análisis de una sola clase de edad, ignoran la covarianza entre tallas de distintos grupos de edad, lo que puede generar distintos resultados cuando se analizan variaciones temporales mediante la comparación de distintos grupos de edad. La posible existencia de errores atípicos en datos de talla por edad, debidos a errores de atribución de edad o errores de medida, puede complicar más la comparación. Proponemos una aproximación en dos etapas para el desarrollo de un indicador para el seguimiento de variaciones temporales o espaciales en datos de talla por edad. Una aproximación robusta, conocida como análisis de elipsoide de volumen mínimo, nos permite identificar los posibles valores aberrantes en datos de talla por edad, y a continuación aplicamos el análisis ponderado de componentes principales a los datos con los valores aberrantes debidamente ponderados. Los componentes principales resultantes permiten definir el indicador para el seguimiento de las variaciones espacio-temporales en datos de talla por edad. Ilustramos la aproximación propuesta con datos por edad de bacalao (Gadus morhua) in el Atlántico noroccidental, correspondiente a la subdivisión 3Ps de la NAFO. El indicador general de talla por edad obtenido muestra que las clases de edad anteriores a 1980 tienden a tener una talla por edad mucho mayor que las clases de edad posteriores a 1980. Article in Journal/Newspaper Gadus morhua Northwest Atlantic Scientia Marina (E-Journal) Sola ENVELOPE(9.806,9.806,63.198,63.198) Scientia Marina 72 2