sm83s1-4999

Mediterranean demersal resources and ecosystems:
25 years of MEDITS trawl surveys

M.T. Spedicato, G. Tserpes, B. Mérigot and E. Massutí (eds)

Explorative analysis on red mullet (Mullus barbatus) ageing data variability in the Mediterranean

Pierluigi Carbonara 1, Walter Zupa 1, Aikaterini Anastasopoulou 2, Andrea Bellodi 3, Isabella Bitetto 1, Charis Charilaou 4, Archontia Chatzispyrou 2, Romain Elleboode 5, Antonio Esteban 6, Maria Cristina Follesa 3, Igor Isajlovic 7, Angélique Jadaud 8, Cristina García-Ruiz 9, Amalia Giannakaki 10, Beatriz Guijarro 11, Sotiris Elias Kiparissis 12, Alessandro Ligas 13, Kelig Mahé 5, Andrea Massaro 14, Damir Medvesek 7, Chryssi Mytilineou 2, Francesc Ordines 11, Paola Pesci 3, Cristina Porcu 3, Panagiota Peristeraki 15,16, Ioannis Thasitis 4, Pedro Torres 9, Maria Teresa Spedicato 1, Angelo Tursi 17, Letizia Sion 17

1 COISPA Tecnologia and Ricerca, Stazione Sperimentale per lo Studio delle Risorse del Mare, Bari, Italy.
(PC) (Corresponding author) E-mail: carbonara@coispa.it. ORCID iD: https://orcid.org/0000-0002-2529-2535
(WZ) E-mail: zupa@coispa.eu. ORCID iD: https://orcid.org/0000-0002-2058-8652
(IB) E-mail: bitetto@coispa.it. ORCID iD: https://orcid.org/0000-0002-8497-1642
(MTS) E-mail: spedicato@coispo.it. ORCID iD: https://orcid.org/0000-0001-9939-9426
2 Hellenic Centre for Marine Research, 46.7 km Athens Sounio ave., P.O. Box 712, 19013 Anavyssos, Attiki, Greece
(AA) E-mail: kanast@hcmr.gr. ORCID iD: https://orcid.org/0000-0003-0872-6984
(AC) E-mail: a.chatzispyrou@hcmr.gr. ORCID iD: https://orcid.org/0000-0003-1603-8524
(CM) E-mail: chryssi@hcmr.gr. ORCID iD: https://orcid.org/0000-0002-9326-1650
3 Department of Life and Environmental Sciences, University of Cagliari, via T. Fiorelli 1, 09126 Cagliari, Italy.
(AB) E-mail: abellodi@unica.it. ORCID iD: https://orcid.org/0000-0002-9017-1692
(MCF) E-mail: follesac@unica.it. ORCID iD: https://orcid.org/0000-0001-8320-9974
(PP) E-mail: ppesci@unica.it. ORCID iD: https://orcid.org/0000-0002-9066-8076
(CP) E-mail: cporcu@unica.it. ORCID iD: https://orcid.org/0000-0003-2649-6502
4 Department of Fisheries and Marine Research, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus.
(CC) E-mail: ccharilaou@dfmr.moa.gov.cy. ORCID iD: https://orcid.org/0000-0002-5510-7265
(IT) E-mail: ithasitis@dfmr.moa.gov.cy. ORCID iD: https://orcid.org/0000-0002-0940-2212
5 French Research Institute for Exploitation of the Sea (Ifremer), Boulogne-sur-Mer, France.
(RE) E-mail: romain.elleboode@ifremer.fr. ORCID iD: https://orcid.org/0000-0001-9084-0895
(KM) E-mail: kelig.mahe@ifremer.fr. ORCID iD: https://orcid.org/0000-0002-6506-211X
6 Instituto Español de Oceanografía (IEO), Centro Oceanográfico de Murcia, Murcia, Spain.
(AE) E-mail: antonio.esteban@ieo.es. ORCID iD: https://orcid.org/0000-0002-2896-7972
7 Institute of Oceanography and Fisheries Split, Šetalište Ivana Meštrovića 63, 21000 Split, Croatia.
(II) E-mail: igor@izor.hr. ORCID iD: http://orcid.org/0000-0001-7101-9575
(DM) E-mail: medvesek@izor.hr. ORCID iD: https://orcid.org/0000-0002-3869-8550
8 MARBEC, Ifremer, Univ Montpellier, CNRS, IRD, 34203 Sète, France.
(AJ) E.mail: Angelique.Jadaud@ifremer.fr. ORCID iD: http://orcid.org/0000-0001-6858-3570
9 Instituto Español de Oceanografía (IEO), Centro Oceanográfico de Málaga, Fuengirola, Málaga, Spain
(CG-R) E-mail: cristina.garcia@ieo.es. ORCID iD: https://orcid.org/0000-0003-2767-4200
(PT) E-mail: pedro.torres@ieo.es. ORCID iD: https://orcid.org/0000-0002-7076-6023
10 Hellenic Center for Marine Research, Institute of Marine Biological Resources and Inland Waters, 71003 Heraklion, Greece.
(AG) E-mail: amalig28@gmail.com. ORCID iD: https://orcid.org/0000-0001-5436-991X
11 Intituto Español de Oceanografía, Centre Oceanogràfic de les Balears, Moll de Ponent s/n, 07015 Palma de Mallorca, Illes Baleares, Spain.
(FO) E-mail: xisco.ordinas@ieo.es. ORCID iD: https://orcid.org/0000-0002-2456-2214
(BG) E-mail: beatriz.guijarro@ieo.es. ORCID iD: https://orcid.org/0000-0002-2083-4681
12 Hellenic Agricultural Organization-DEMETER, Fisheries Research Institute of Kavala, 64007 Nea Peramos, Kavala, Greece.
(SEK) E-mail: skipariss@hcmr.gr. ORCID iD: https://orcid.org/0000-0002-0587-8889
13 Centro Interuniversitario di Biologia Marina ed Ecologia Applicata, Viale Nazario Sauro 4, 57128 Livorno, Italy.
(AL) E-mail: ligas@cibm.it. ORCID iD: https://orcid.org/0000-0003-1036-3553
14 APLYSIA, Via Menichetti 35, 57121 Livorno, Italy.
(AM) E-mail: andrea.massaro@aplysia.it. ORCID iD: https://orcid.org/0000-0001-9224-3883
15 Hellenic Center for Marine Research, Iraklion, Crete, Greece.
16 University of Crete, Biology Department, Stavrakia, Heraklion, Crete.
(PP) E-mail: notap@hcmr.gr. ORCID iD: https://orcid.org/0000-0002-8608-078X
17 University of Bari Aldo Moro (UNIBA), Department of Biology, Via Orabona 4, 70125 Bari, Italy.
(AT) E-mail: angelo.tursi@uniba.it. ORCID iD: https://orcid.org/0000-0002-7776-2738
(LS) E-mail: letizia.sion@uniba.it. ORCID iD: https://orcid.org/0000-0002-0308-1841

Summary: The uncertainty in age estimation by otolith reading may be at the root of the large variability in red mullet (Mullus barbatus) growth models in the Mediterranean. In the MEDITS survey, red mullet age data are produced following the same sampling protocol and otolith reading methodology. However, ageing is assigned using different interpretation schemes, including variations in theoretical birthdate and number of false rings considered, in addition to differences in the experience level of readers. The present work analysed the influence of these variations and the geographical location of sampling on red mullet ageing using a multivariate approach (principal component analysis). Reader experience was the most important parameter correlated with the variability. The number of rings considered false showed a significant effect on the variability in the first age groups but had less influence on the older ones. The effect of the theoretical birthdate was low in all age groups. Geographical location had a significant influence, with longitude showing greater effects than latitude. In light of these results, workshops, exchanges and the adoption of a common ageing protocol based on age validation studies are considered fundamental tools for improving precision in red mullet ageing.

Keywords: Mullus barbatus; age variability; MEDITS; Mediterranean, reader effect, false rings, date of birth.

Análisis exploratorio de los datos de determinación de edad de Mullus barbatus en el Mediterráneo

Resumen: La incertidumbre en la estimación de la edad mediante la lectura de otolitos puede ser la principal causa detrás de la gran variabilidad existente en los modelos de crecimiento del salmonete (Mullus barbatus) en el Mediterráneo. En la campaña MEDITS, los datos de edad del salmonete se generan siguiendo el mismo protocolo de muestreo y metodología de lectura de otolitos, aunque la asignación de la edad se lleva a cabo usando distintos esquemas para la interpretación de las lecturas, incluyendo variaciones en la fecha teórica de nacimiento y en el número de anillos considerados falsos, además de las diferencias existentes en cuanto al nivel de experiencia de los lectores. En este trabajo, la influencia de las variaciones en los esquemas de interpretación, el nivel de experiencia de los lectores, y la localización geográfica de las muestras, sobre la determinación de la edad en el salmonete se analiza mediante una aproximación multivariante (Análisis de Componentes Principales). La experiencia de los lectores fue el parámetro más correlacionado con la variabilidad. El número de anillos considerados falsos mostró un efecto significativo sobre la variabilidad de los primeros grupos de edad, mientras que su influencia sobre los más viejos fue menor. El efecto de la fecha teórica de nacimiento tuvo poca importancia en todos los grupos de edad. La localización geográfica tuvo una influencia significativa, con la longitud mostrando mayores efectos que la latitud. Teniendo en cuenta estos resultados, los grupos de trabajo e intercambios de otolitos así como la adopción de un protocolo de asignación de edad común basado en estudios de validación de edad, se consideran herramientas fundamentales para mejorar la precisión en la determinación de la edad del salmonete.

Palabras clave: Mullus barbatus; variabilidad en determinación de edad; MEDITS; mar Mediterráneo; efecto del lector; anillos falsos; fecha de nacimiento.

Citation/Cómo citar este artículo: Carbonara P., Zupa W., Anastasopoulou A., Bellodi A., Bitetto I., Charilaou C., Chatzispyrou A., Elleboode R., Esteban A., Follesa M.C., Isajlovic I., Jadaud A., García-Ruiz C., Giannakaki A., Guijarro B., Kiparissis S.E., Ligas A., Mahé K., Massaro A., Medvesek D., Mytilineou C., Ordines F., Pesci P., Porcu C., Peristeraki P., Thasitis I., Torres P., Spedicato M.T., Tursi A., Sion L. 2019. Explorative analysis on red mullet (Mullus barbatus) ageing data variability in the Mediterranean. Sci. Mar. 83S1: 271-279. https://doi.org/10.3989/scimar.04999.19A

Editor: E. Massutí.

Received: March 6, 2019. Accepted: September 21, 2019. Published: October 30, 2019.

Copyright: © 2019 CSIC. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Contents

Summary
Resumen
Introduction
Materials and methods
Results
Discussion
Acknowledgements
References

INTRODUCTIONTop

Age and growth data are among the most important data input in stock assessment analytical models (Reeves 2003Reeves S.A. 2003. A simulation study of the implications of age-reading errors for stock assessment and management advice. ICES J. Mar. Sci. 60: 314-328.). However, bias in these data can lead to stock diagnosis failures (Eero et al. 2015Eero M., Hjelm J., Behrens J., et al. 2015. Eastern Baltic cod in distress: biological changes and challenges for stock assessment. ICES J. Mar. Sci. 72: 2180-2186.). Poor quality ageing data have also contributed in certain cases to misleading evaluation of the population status, sometimes resulting in stock collapse (Beamish and McFarlane 1995Beamish R.J., McFarlane G.A. 1995. A discussion of the importance of ageing error, and an application to walleye Pollock: the world’s largest fishery. In: Secor D.H., Dean J.M. et al. (eds), Recent developments in fish otolith research. Univ. South Carolina Press, Columbia, pp. 545-565., Liao et al. 2013Liao H., Sharov A.F., Jones C.M., et al. 2013. Quantifying the Effects of Aging Bias in Atlantic Striped Bass Stock Assessment. Trans. Am. Fish. Soc. 142: 193-207.). For these reasons, an increasing effort has been devoted during the last few decades to improving the quality of age data (ICES 2011aICES. 2011a. Report of the Workshop of National Age Readings Coordinators (WKNARC), 5-9 September 2011, Boulogne-sur-Mer, France. ICES CM 2011/ACOM:45, 175 pp., 2013ICES. 2013. Report of the Second Workshop of National Age Readings Coordinators (WKNARC2), 13 - 17 May 2013, Horta, Azores. ICES CM 2013/ACOM:52, 65 pp.), especially in the context of the European Union Data Collection Framework, which is implementing otolith exchange exercises, workshops and meetings concerning the ageing of the most important species in the European fisheries (ICES 2018ICES. 2018. Working Group on Biological Parameters (WGBIOP), 1-5 October 2018. Ghent, Belgium. ICES CM 2018/EOSG:07, 186 pp.).

Several of these workshops (ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.) were dedicated to the ageing analysis of red mullet (Mullus barbatus), one of the most important species in terms of landings for the Mediterranean fisheries (FishStat 2016Fishstat. 2016. Fisheries and aquaculture software. FishStat Plus - Universal software for fishery statistical time series. FAO Fisher. Aquac. Dep. Rome. Updated 14 September 2017. [Cited 10 October 2019].). Despite the number of workshops and exchange exercises done (Mahé et al. 2012Mahé K., Elleboode R., Charilaou C., et al. 2012. Striped red mullet (Mullus surmuletus) and red mullet (M. barbatus) otolith and scale exchange 2011. Ifremer 30 pp., 2016Mahé K., Anastasopoulou A., Bekas P., et al. 2016. Report of the Striped red mullet (Mullus surmuletus) and Red mullet (Mullus barbatus) Exchange 2016. Ifremer 21 pp.), the precision in M. barbatus ageing, in terms of percentage of agreement and coefficient of variation, is still outside acceptable limits (ICES 2011bICES. 2011b. Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS), 7 - 11 February 2011, Vienna, Austria. ICES CM 2011/ACOM:40, 174 pp.). Several sources of disagreement have been recognized during these ageing workshops: i) the identification of the first growth ring with an annual periodicity; ii) the number (one or two) of false increments; iii) the presence/absence of reproductive ring(s) after the first growth ring; iv) disagreement considering the age assignment (theoretical birthdate 1 January versus 1 July); and v) the overlapping of the annulus in the older specimens (ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., 2017aICES. 2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.). All these issues can result in high red mullet ageing differences (ICES 2017aICES. 2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.). While reviewing age data on this species published during the last decade, Carbonara et al. (2018)Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219. observed an impressively varying average length at the first year, ranging between 7.54 and 18.93 cm. This high variability is difficult to justify through only the geographical and/or genetic differences (Matić-Skoko et al. 2018Matić-Skoko S., Šegvić-Bubić T., Mandić I., et al. 2018. Evidence of subtle genetic structure in the sympatric species Mullus barbatus and Mullus surmuletus (Linnaeus, 1758) in the Mediterranean Sea. Sci. Rep. 8: 676.). Another source of difference in the age analysis can be the different calcified structures used in age reading (scales or otoliths). In particular, scale reading may cause the underestimation of age in the larger specimens of the species (ICES 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., Mahé et al. 2012Mahé K., Elleboode R., Charilaou C., et al. 2012. Striped red mullet (Mullus surmuletus) and red mullet (M. barbatus) otolith and scale exchange 2011. Ifremer 30 pp.). Consequently, otoliths are considered the most suitable calcified structures for red mullet ageing (ICES 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp.). Moreover, the growth studies based on indirect methods (length frequency distribution analysis) provided a faster growth pattern than the analysis based on direct age analysis from otolith and scale reading (Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.). The uncertainties linked to age analysis are also hampered by the lack of direct (e.g. mark-recapture, radiochemical dating), indirect (e.g. length frequency distribution analysis) and semi-direct (e.g. marginal analysis; marginal increment analysis) validation studies (Bianchini and Ragonese 2011Bianchini M.L., Ragonese S. 2011. Establishing length-at-age references in the red mullet, Mullus barbatus L. 1758 (pisces, Mullidae), a case study for growth assessments in the Mediterranean geographical sub-areas (GSA). Mediterr. Mar. Sci. 12: 316-332., Sieli et al. 2011Sieli G., Badalucco C., Di Stefano G., et al. 2011, Biology of red mullet, Mullus barbatus (L. 1758), in the Gulf of Castellammare (NW Sicily, MediterraneanSea) subject to a trawling ban. J. Appl. Ichthyol. 27: 1218-1225.). Indeed, direct validation is challenging because of the difficulty of catching specimens alive (Düzbastilar et al. 2015Düzbastilar F.O., Laleli T., Özgül A., et al. 2015. Determining the severity of skin injuries of red mullet, Mullus barbatus (Actinopterygii: Perciformes: Mullidae), inflicted during escape from trawl codend. Acta Ichthyol. Piscat. 45: 75-83.) and the relative short life span of red mullet (Vrantzas et al. 1992Vrantzas N., Kalagia M., Karlou C. 1992. Age, growth and state of stock of red mullet (Mullus barbatus L. 1758) in the Saronikos Gulf of Greece. FAO Fish. Rep. 477: 51-67., Sieli et al. 2011Sieli G., Badalucco C., Di Stefano G., et al. 2011, Biology of red mullet, Mullus barbatus (L. 1758), in the Gulf of Castellammare (NW Sicily, MediterraneanSea) subject to a trawling ban. J. Appl. Ichthyol. 27: 1218-1225.). Only two validation studies have been performed so far and are available in the literature: one in the southern Tyrrhenian Sea (Sieli et al. 2011Sieli G., Badalucco C., Di Stefano G., et al. 2011, Biology of red mullet, Mullus barbatus (L. 1758), in the Gulf of Castellammare (NW Sicily, MediterraneanSea) subject to a trawling ban. J. Appl. Ichthyol. 27: 1218-1225.) focusing only on the periodicity of the growth increment deposition (marginal analysis) and one in the southern Adriatic (Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.) focusing on the periodicity of the growth increment deposition (marginal analysis and marginal increment analysis) and the indirect validation method (length frequency distribution analysis).

The impact of age analysis uncertainties on the stock assessment of red mullet can be significant, since the identification of the first growth ring (ICES 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp.) can lead to the over-/underestimation of one or more year in age (Vrantzas et al. 1992Vrantzas N., Kalagia M., Karlou C. 1992. Age, growth and state of stock of red mullet (Mullus barbatus L. 1758) in the Saronikos Gulf of Greece. FAO Fish. Rep. 477: 51-67., Sieli et al. 2011Sieli G., Badalucco C., Di Stefano G., et al. 2011, Biology of red mullet, Mullus barbatus (L. 1758), in the Gulf of Castellammare (NW Sicily, MediterraneanSea) subject to a trawling ban. J. Appl. Ichthyol. 27: 1218-1225.). In general, the growth of this species follows a biphasic pattern: it is high in the first year, reaching about a third of the maximum size, and once it has reached the size at the first maturity, there is a significant decrease in the growth rate (Fiorentino et al. 1998Fiorentino F., Zamboni A., Rossi M., et al. 1998. The growth of the Red Mullet (Mullus barbatus, L. 1758) during the first years of life in the Ligurian Sea (Mediterranean). CHIEAM-Options Mediterr. 35: 65-78., 2013Fiorentino F., Gancitano V., Gancitano S., et al. 2013. An updated two phase growth model for demersal fish with an application to red mullet (Mullus barbatus L., 1758) (Perciformes Mullidae) of the Mediterranean. Naturalista Sicil. S. IV, XXXVII: 529-542., Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.).

Analysing the percentage of agreement (PA) and coefficient of variation (CV) among the readers obtained by the workshops/exchanges on age reading of M. barbatus in the last decade (ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.), an improvement in precision was obtained (PA, from 51.6% in Exchange 2008 to 67% in Exchange 2016; CV, from 68.5% in Exchange 2008 to 64.6% in Exchange 2016), but not sufficient to reach the acceptable threshold limit of precision (PA 80%, CV 20%) (ICES 2011bICES. 2011b. Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS), 7 - 11 February 2011, Vienna, Austria. ICES CM 2011/ACOM:40, 174 pp.). Most of the readers who participated in the exchanges contributed their age readings to the stock assessment analysis. In this context, a common ageing protocol is an important tool for decreasing the relative/absolute bias, improving precision (reducing the CV and increasing the PA) (ICES 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.) in age determination, and increasing reproducibility among the age readers of different laboratories (ICES 2011bICES. 2011b. Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS), 7 - 11 February 2011, Vienna, Austria. ICES CM 2011/ACOM:40, 174 pp.). Therefore, in order to reach this goal, it is useful to assess the effect of the specific factors influencing the age reading variability (i.e. theoretical birthdate, ageing criteria, age scheme, reader experience).

Red mullet otoliths have been collected since 2012 throughout the European Mediterranean waters during the international MEDITS bottom trawl survey. However, individual age determination is affected by sources of variation, including different ageing schemes, different reader experience and geographical differences in growth (Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219., Sonin et al. 2007Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228). The objective of this work is to investigate the potential influence of these factors on red mullet ageing in the Mediterranean using a multivariate approach. Results could be useful for defining a standardized reading protocol aimed at obtaining unbiased age-length keys for red mullet stocks in the Mediterranean.

MATERIALS AND METHODSTop

Data

The red mullet otoliths used in this study were collected during the MEDITS surveys, which are carried out in spring and early summer (usually from April to July) following a standardized sampling protocol (Anonymous 2017Anonymous. 2017. MEDITS-Handbook. Version nº 9. MEDITS Working Group, 106 pp.). In our analysis, we used the otolith reading (length/age) data collected in 2014 surveys at geographical sub-areas (GSAs) established by the General Fisheries Commission for the Mediterranean (Fig. 1).

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Fig. 1. – Map of the study area showing the 15 geographical sub-areas (GSAs) established by the General Fisheries Commission for the Mediterranean http://www.gfcm.org) where otoliths of red mullet (Mullus barbatus) were sampled.

For each GSA age data set, we considered the following meta-data: sex, theoretical birth date applied for ageing (1 January or 1 July), reader experience (low, <2000 otolith readings; medium, 2000-6000 otolith readings; high, >6000 otolith readings), number of false rings considered before the first winter ring (0, 1 or 2) and geographic location as an average between the latitude and longitude of each GSA. The readers analysed the otoliths from their own area, providing information on the theoretical birth date applied, reader experience and number of false rings considered. In total, the scientific staff from 13 laboratories (GSAs 7 and 8 and GSAs 10 and 18 were analysed by the same scientific staff) analysed 5055 pairs of otoliths (2815 female and 2240 male) collected in 15 GSAs (Fig. 2).

figure2

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Fig. 2. – Length-at-age data by geographical sub-area (GSA) and sex, estimated from otolith readings of red mullet (Mullus barbatus) and used in the present study.

Multivariate analysis

Principal component analysis (PCA) was applied to identify the most informative variables influencing the differences in the ageing data of red mullet. PCA is a multivariate statistical technique (Jolliffe 2002Jolliffe I.T. 2002. Principal Component Analysis. Second Edition, Springer Series in Statistics, 487 pp., Abdi and Williams 2010Abdi H., Williams L.J. 2010. Principal component analysis. WIREs Computational Statistics 2: 433-459.) used to extract the important information from a multivariate data set and express this information as a set of a few new variables called principal components (PCs). The PCs are calculated as linear combinations of the original variables aimed at identifying directions (or PCs), along which the variation in the data is maximized. Hence the number of selected PCs is less than or equal to the number of original variables. The information in a given data set corresponds to the total variation it contains. The PCA aims to identify the directions (or PCs) along which the variation in the data is largest. To measure the effects of each variable in the system, the correlation level with PCs is used. In other words, PCA reduces the dimensionality of a multivariate data set to a lower number of principal components with minimal loss of information (Kassambara 2017Kassambara A. 2017. Practical Guide To Principal Component Methods in R: PCA, M (CA), FAMD, MFA, HCPC, factoextra (Vol. 2). Statistical tools for high-throughput data analysis - STHDA, 169 pp.). PCA was performed using the FactoMineR library (Lê et al. 2008Lê S., Josse J., Husson F. 2008. FactoMineR: An R Package for Multivariate Analysis. J. Stat. Softw. 25.) available in R (R Development Core Team 2018R Development Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.). The main feature of the FactoMineR library is the ability to perform the analysis using different types of variables (quantitative or categorical).

The first analysis considered all the age groups together, using total length (TL), age of the specimens and GSA coordinates (latitude and longitude) as quantitative variables and number of false rings, sex, theoretical birthdate and reader experience as qualitative variables. The age groups from 0 to 4 were the most represented in the data set, while the age groups of more than 4 years were present only in 9 GSAs (Table 1). Thus, ten PCAs were performed for each age group from 0 to 4 years and for both sexes using TL and GSAs coordinates as quantitative variables and theoretical birthdate, reader experience and number of false rings as qualitative variables. The temporal factor was not included in the analysis, because all data were collected in 2014 during a short period (April–July) as foreseen by the MEDITS protocol (Anonymous 2017Anonymous. 2017. MEDITS-Handbook. Version nº 9. MEDITS Working Group, 106 pp.).

Table 1. – Number of otoliths (n) of red mullet analysed by geographical sub-area (GSA), with information on total length (TL, cm), age range (years), birthdate (Jul, 1 July; Jan, 1 January), number of false rings (FR) and level of experience of readers (Exp; L, low: <2000 otolith readings; M, medium: 2000-6000 otolith readings; H, high: >6000 otolith readings).

GSA n TL Age Birthday FR Exp
1 151 10.9-24.1 0-3 Jul 1 M
5 49 11.1-22.2 0-3 Jul 1-2 M
6 113 11.2-25.7 0-5 Jul 1-2 M
7 681 10-30 1-5 Jul 1 H
8 432 9.5-21 1-6 Jan 1 H
9 242 5-26.5 0-5 Jan 1 M
10 469 5.5-24.5 0-6 Jul 1 M
11 413 10.2-23.1 0-4 Jul 1 M
17 354 10-25 0-7 Jul 2 M
18 679 4-24.5 0-5 Jul 1 H
19 525 8.7-22.9 0-5 Jul 1 M
20 59 11.3-18.9 1-3 Jan 1-3 H
22 402 4.7-23.5 0-5 Jul 0-3 L
23 294 6.1-21.6 0-4 Jul 0-2 L
25 192 8.6-19.9 1-4 Jul 1 M

The number of PCs to be considered for each PCA was determined using the Kaiser rule (Kaiser 1960Kaiser H.F. 1960. The Application of Electronic Computers to Factor Analysis’. Educ. Psichol. Meas. 20: 141-151.), retaining only those PCs whose variance exceeds 1. PCs with variance lower than 1 are less informative and thus not worth being retained (Jolliffe 2002Jolliffe I.T. 2002. Principal Component Analysis. Second Edition, Springer Series in Statistics, 487 pp.). Only significant correlations (p<0.05) were considered in the analysis.

RESULTSTop

In the PCA performed on the whole dataset, the first two PCs were retained, accounting for 84.5% of the total variability (Table 2). The first principal component (PC1) was strongly correlated with all four original variables (TL, age, latitude and longitude), with TL showing the highest correlation (Table 3). Size, age and latitude variables varied together, being positively correlated with PC1. In contrast, longitude was an opposite effect.

Table 2. – Values of variance (Var), percentage of variance (%Var) and cumulative percentage of variance (Σ%Var) that accounted for each dimension (Dim) in the PCAs.

Sex and age Variables Dim 1 Dim 2 Dim 3 Dim 4
Both 0-4 Var 2.11 1.27 0.50 0.12
%Var 52.86 31.63 12.52 3.00
Σ%Var 52.86 84.49 97.00 100.00
Females 0 Var 1.66 1.17 0.18 -
%Var 55.18 38.92 5.91 -
Σ%Var 55.18 94.10 100.00 -
Females 1 Var 1.82 0.96 0.22 -
%Var 60.67 32.03 7.30 -
Σ%Var 60.67 92.70 100.00 -
Females 2 Var 2.05 0.76 0.19 -
%Var 68.29 25.30 6.41 -
Σ%Var 68.29 93.59 100.00 -
Females 3 Var 1.65 1.14 0.22 -
%Var 54.95 37.89 7.16 -
Σ%Var 54.95 92.84 100.00 -
Females 4 Var 1.51 1.32 0.18 -
%Var 50.34 43.84 5.82 -
Σ%Var 50.34 94.18 100.00 -
Males 0 Var 1.67 1.13 0.20 -
%Var 55.61 37.78 6.61 -
Σ%Var 55.61 93.39 100.00 -
Males 1 Var 1.79 1.02 0.19 -
%Var 59.71 33.92 6.37 -
Σ%Var 59.71 93.63 100.00 -
Males 2 Var 1.96 0.90 0.14 -
%Var 65.44 29.87 4.69 -
Σ%Var 65.44 95.31 100.00 -
Males 3 Var 1.67 1.05 0.28 -
%Var 55.68 34.92 9.40 -
Σ%Var 55.68 90.60 100.00 -
Males 4 Var 1.48 1.25 0.27 -
%Var 49.21 41.82 8.98 -
Σ%Var 49.21 91.02 100.00 -

Table 3. – Summary of the correlation coefficients of both continuous (TL, total length; Lat, latitude; Lon, longitude; Age of the specimens) and qualitative (Exp, reader experience; NFR, number of false rings; B, theoretical birthdate; Sex) variables (VAR) for dimensions (Dim) 1 and 2 in the PCAs. Non-significant correlations (p>0.05) are shown in bold.

Both Sexes / Ages 0-4 Females Males
VAR Dim 1 Dim 2 Age VAR Dim 1 Dim 2 Age VAR Dim 1 Dim 2
TL 0.82 0.43 0 TL 0.81 –0.53 0 TL 0.88 –0.38
Lat 0.72 –0.51 Lat 0.32 0.93 Lat 0.15 0.97
Lon –0.61 0.69 Lon –0.95 –0.14 Lon –0.93 –0.20
Age 0.75 0.58 Exp 0.69 0.47 Exp 0.66 0.52
Exp 0.28 0.14 NFR 0.37 0.06 NFR 0.34 0.10
NFR 0.15 0.01 B 0.02 0.37 B 0.00 0.38
B 0.12 0.14 1 TL 0.45 0.88 1 TL 0.19 0.98
Lat 0.86 –0.42 Lat 0.92 –0.24
Lon –0.94 0.04 Lon –0.95 –0.04
Exp 0.39 0.13 Exp 0.44 0.10
NFR 0.04 0.01 NFR 0.04 0.00
B 0.23 0.11 B 0.28 0.03
2 TL 0.67 0.73 2 TL 0.46 0.88
Lat 0.84 –0.47 Lat 0.92 –0.31
Lon –0.94 0.10 Lon –0.95 0.13
Exp 0.19 0.14 Exp 0.28 0.10
NFR 0.01 0.04 NFR 0.02 0.06
B 0.26 0.07 B 0.29 0.00
3 TL 0.33 0.92 3 TL 0.14 0.98
Lat 0.80 –0.52 Lat 0.89 –0.28
Lon –0.94 –0.12 Lon –0.92 –0.11
Exp 0.34 0.12 Exp 0.16 0.05
NFR 0.02 0.29 NFR 0.06 0.15
B 0.31 0.00 B 0.23 0.08
4 TL 0.46 0.86 4 TL 0.45 0.85
Lat 0.61 –0.75 Lat 0.62 –0.73
Lon –0.96 –0.07 Lon –0.94 –0.07
Exp 0.31 0.24 Exp 0.03 0.47
NFR 0.00 0.66 NFR 0.00 0.62
B 0.45 0.05 B 0.52 0.09

Although none of the qualitative variables showed a strong correlation with PC1, the highest contribution was shown by reader experience, followed by number of false rings and birth date. Unlike latitude, longitude showed a higher correlation with the second principal component (PC2). The qualitative variables showed the following order of decreasing correlation with PC2: reader experience, birth date and sex.

The PCAs performed on each age group and sex showed a strong geographical effect mostly driving PC1. Indeed, longitude and latitude were the best-correlated variables in almost all the age groups, at least in the PC1, but with opposite directions. Moreover, TL was mostly correlated with PC2, except for the age group 0, in which latitude had the highest correlation value.

Among the qualitative variables, the highest correlation with PC1 was shown by reader experience, especially in the lower age classes, and birthdate, mostly in the oldest age groups (Fig. 3). The contribution of number of false rings was important for the age groups 0 (PC1) and 4 (PC2).

figure3

Full size image

Fig. 3. – Confidence ellipses drawn around the levels of the categorical variables considered in each PCA (confidence level = 0.95) by sex and age group, taking into account the variables theoretical birthdate (1 January, 1 July), reader experience (L, low: <2000 otolith readings; M, medium: 200-5000 otolith readings; H, high: >5000 otolith readings) and number of false rings considered before the first winter growth increment (A, 0; B, 1; C, 2; D, 3).

DISCUSSIONTop

The results of the present paper confirm the high variability occurring in the age and growth of the red mullet in the Mediterranean reported in previous studies (Bianchini and Ragonese 2011Bianchini M.L., Ragonese S. 2011. Establishing length-at-age references in the red mullet, Mullus barbatus L. 1758 (Pisces, Mullidae), a case study for growth assessments in the Mediterranean geographical sub-areas (GSA). Mediterr. Mar. Sci. 12: 316-332., Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.). The variability in age data can be attributed to several factors, including the use of different sampling methodologies (commercial fishing or scientific surveys: Coggins et al. 2013Coggins L.G., Gwinn D.C., Allen M.S. 2013. Evaluation of Age-Length Key Sample Sizes Required to Estimate Fish Total Mortality and Growth. Trans. Am. Fish. Soc. 142: 832-840.), age schemes (ICES 2011aICES. 2011a. Report of the Workshop of National Age Readings Coordinators (WKNARC), 5-9 September 2011, Boulogne-sur-Mer, France. ICES CM 2011/ACOM:45, 175 pp.), otolith preparation methods (Smith et al. 2016Smith B.J., Dembkowski D.J., James D.A., et al. 2016. A Simple Method to Reduce Interpretation Error of Ages Estimated from Otoliths. Open Fish Sci. J. 9: 1-7.), age criteria (Hüssy et al. 2016Hüssy K., Radtke K., Plikshs M., et al. 2016. Challenging ICES age estimation protocols: lessons learned from the eastern Baltic cod stock. ICES J. Mar. Sci. 73: 2138-2149.), reader experience (Kimura and Lyons 1991Kimura D.K., Lyons J.J. 1991. Between-Reader Bias and Variability in the Age-Determination Process. Fish. Bull. 89: 53-60.) and geographical differences (Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.). These factors affect both the accuracy and the precision of the age and growth data, having a negative impact on stock status assessment and the application of management measures aiming to achieve a sustainable exploitation of red mullet in the Mediterranean. Most of the stock assessment models used, especially the analytical ones such as virtual population analysis (e.g. Extended Survivor Analysis) and statistical catch-at-age (e.g. the state-space assessment model and assessment-for-all), require knowledge of the demographic structure of the stocks. One of the first steps for running these models is the conversion of the LFD of catches to age structure, which is performed by means of age slicing procedures using growth parameters from the von Bertalanffy growth function (VBGF) or age-length keys. Inappropriate growth parameters or age-length keys to convert size distribution into age structure can lead to unreliable scientific advice (STECF 2017Scientific Technical and Economic Committee for Fisheries (STECF). 2017. 54th Plenary Meeting Report (PLEN-17-01). Publ. Offi. Europ. Union, Luxembourg; EUR 28569 EN). If an age overestimation occurs, the stock assessment will provide an erroneous scenario with a population composed of older individuals and, consequently, affected by lower fishing mortality, while in the opposite case, fish would be younger with an overestimation of fishing mortality (Campana 2001Campana S.E. 2001. Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish Biol. 59: 197-242.). Moreover, age and growth also affect the estimation of natural mortality and maturity-at-age data. As a result, they can affect the estimation of recruitment strength and spawning stock biomass. Ultimately, the most important effect is linked to short-term predictions of the stock status and the related management measures (Punt et al. 2008Punt A.E., Smith D.C., KrusicGolub K., et al. 2008. Quantifying age-reading error for use in fisheries stock assessments, with application to species in Australia’s southern and eastern scalefish and shark fishery. Can. J. Fish. Aquat. Sci. 65: 1991-2005., Eero et al. 2015Eero M., Hjelm J., Behrens J., et al. 2015. Eastern Baltic cod in distress: biological changes and challenges for stock assessment. ICES J. Mar. Sci. 72: 2180-2186., Hüssy et al. 2016Hüssy K., Radtke K., Plikshs M., et al. 2016. Challenging ICES age estimation protocols: lessons learned from the eastern Baltic cod stock. ICES J. Mar. Sci. 73: 2138-2149.).

Our findings revealed that samples geographical location was the most important factor significantly correlated to age variability, in particular the longitudinal (west-east) influenced more than latitudinal (north-south) samples geographical component. Considering the relative genetic homogeneity of this species in the area (Matić-Skoko et al. 2018Matić-Skoko S., Šegvić-Bubić T., Mandić I., et al. 2018. Evidence of subtle genetic structure in the sympatric species Mullus barbatus and Mullus surmuletus (Linnaeus, 1758) in the Mediterranean Sea. Sci. Rep. 8: 676.), the detected ageing variability could be attributed to geographical and environmental differences. A reduction in the growth rate of red mullet from west to east in the Mediterranean has already been described (Sonin et al. 2007Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228., Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.). In support of this observation comes the hypothesis by Sonin et al. (2007)Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228. for red mullet called “Levantine nanism”, according to which specimens are characterized by smaller body size in the Levantine basin than the conspecifics in the western Mediterranean. These findings can be explained by the lower productivity of the eastern Mediterranean compared with the western basin, with a higher chlorophyll concentration in the western than the eastern part (Moutin and Raimbault 2002Moutin T., Raimbault P. 2002. Primary production, carbon export and nutrients availability in western and eastern Mediterranean Sea in early summer 1996 (MINOS cruise). J. Mar. Syst. 33-34: 273-288.). The higher water temperature in the southeastern Mediterranean may be another explanation for Levantine nanism (Sonin et al. 2007Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228.). The higher water temperature may result in a higher metabolic rate in that area for the red mullet, resulting in earlier sexual maturity and a consequent decrease in growth rate (Saborido-Rey and Kjesbu 2005Saborido-Rey F., Kjesbu O.S. 2005. Growth and maturation dynamics. 26 pp.). Other environmental factors such as salinity, food competition and invasive species could also be factors driving to dwarfism (Sonin et al. 2007Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228., Edelistet al. 2014Edelist D., Golani D., Spanier E. 2014. First implementation of the Large Fish Index (LFI) in the eastern Mediterranean. Sci. Mar. 78: 185-192., Corrales et al. 2017Corrales X., Ofir E., Coll M., et al. 2017. Modeling the role and impact of alien species and fisheries on the Israeli marine continental shelf ecosystem. J. Mar. Syst. 170: 88-102.).

Reader experience has been identified as an important factor affecting the precision of age data for many species in both marine and freshwater environments (Appelberg et al. 2005Appelberg M., Formigo N., Geffen A.J., et al. 2005. A cooperative effort to exchange age reading experience and protocols between European fish institutes. Fish. Res. 76: 167-173., Kimura and Anderl 2005Kimura D.K., Anderl D.M. 2005 Quality control of age data at Alaska Science Center. Mar. Freshwater Res. 56: 783-789., Rude et al 2013Rude N.P., Hintz W.D., Norman J.D., et al. 2013. Using pectoral fin rays as a non lethal aging structure for smallmouth bass: precision with otolith age estimates and the importance of reader experience. J. Freshwater Ecol. 28: 2-199.). In the present study, this factor was also found to be important in ageing variability of red mullet in the Mediterranean, especially when we compared the results of readers with high-medium vs. low experience. This factor emerged as a key issue in estimating the age mostly in the youngest ages (0 and 1 years) and oldest ages (4 years). The identification of the true first growth increment and the overlapping of the growth rings have been mentioned as reasons for disagreement in ageing analysis of this species (ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.). However, the precision of these results was calculated for all readers together, without an assessment of reader experience because of the low number of readers (Mahé et al. 2012Mahé K., Elleboode R., Charilaou C., et al. 2012. Striped red mullet (Mullus surmuletus) and red mullet (M. barbatus) otolith and scale exchange 2011. Ifremer 30 pp., 2016Mahé K., Anastasopoulou A., Bekas P., et al. 2016. Report of the Striped red mullet (Mullus surmuletus) and Red mullet (Mullus barbatus) Exchange 2016. Ifremer 21 pp., ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.).

The theoretical birthdate has also been reported as another important element in the process of age estimation (Morales-Nin and Panfili 2002Morales-Nin B., Panfili J. 2002. Specific age estimation. In: Panfili J., Troadec H., et al. (eds), Manual of fish sclerochronology. Ifremer-IRD coedition, Brest, France.). In our analysis, birthdate had a lower influence in the first age group than reader experience and number of false rings. In the rest of the age groups, birthdate had a greater influence on ageing. The specific date of birth at individual and/or population level, as established by studies of reproduction and/or analysis of daily increments, is not always known. Therefore, for convenience during the stock assessment process the conventional birthdate for the entire population was established at 1 January (Morales-Nin and Panfili 2002Morales-Nin B., Panfili J. 2002. Specific age estimation. In: Panfili J., Troadec H., et al. (eds), Manual of fish sclerochronology. Ifremer-IRD coedition, Brest, France.). The reproduction of red mullet in the Mediterranean takes place from April to September (Carbonara et al. 2015Carbonara P., Intini S., Modugno E., et al. 2015. Reproductive biology characteristics of red mullet (Mullus barbatus L., 1758) in Southern Adriatic Sea and management implications. Aquat. Living Resour. 28: 21-31.). Thus, an age scheme based on 1 July as the birthdate of the species has been suggested as more appropriate, avoiding overestimation of age in the first year. Considering 1 January as the birthdate, specimens born during the spawning season (April-September) will be aged as 1 year old, even if they are caught after 6 months. During the last workshop on red mullet age validation, the use of a single ageing scheme based on the birthdate of 1 July was endorsed with agreement among all readers in order to overcome this kind of bias (ICES 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.). Nevertheless, though the mid-year birthdate is consistent with the species, it could generate a mismatch between age groups and year classes for the year time-step on which the assessment is run. For this reason, for other species, such as anchovy (Engraulis encrasicolus), despite a peak of spawning in early summer, a birthdate of 1 January has been adopted (ICES 2017bICES. 2017b. Report of the Workshop on Age estimation of European anchovy (Engraulis encrasicolus). WKARA2 2016 Report 28 November - 2 December 2016. Pasaia, Spain. ICES CM 2016/SSGIEOM:17, 223 pp.). Thus, the annual catches-at-age obtained by slicing the LFDs using VBGF parameters and/or age-length keys correspond directly to the annual catch (ICES 2017bICES. 2017b. Report of the Workshop on Age estimation of European anchovy (Engraulis encrasicolus). WKARA2 2016 Report 28 November - 2 December 2016. Pasaia, Spain. ICES CM 2016/SSGIEOM:17, 223 pp.). However, it must be considered that birthdate is also an important factor in the estimation of spawning stock biomass (SSB). In fact, the SSB estimated for the real spawning period (in the case of a birthdate of 1 January, before the spawning period) may lead to an overestimation of this stock variable because of the underestimation of natural and fishing mortality before the spawning period.

The interpretation of the first growth ring has been mentioned as another source of discrepancy among readers (ICES 2009ICES. 2009. Report of the Workshop on Age Reading of Red mullet Mullus barbatus and Striped mullet Mullus surmuletus (WKACM), 30 March - 3 April 2009, Boulogne sur Mer, France. ICES CM 2009\ACOM:44, 42 pp., 2012ICES. 2012 Report of the Workshop on Age Reading of Red Mullet and Striped Red Mullet (WKACM), 2-6 July 2012, Boulogne sur Mer, France. ICES CM 2012\ACOM: 60, 52 pp., 2017aICES.2017a. Workshop on Ageing Validation methodology of Mullus species (WKVALMU), 15-19 May 2017, Conversano, Italy. ICES CM 2017/ SSGIEOM:31, 74 pp.). In particular, two different hypotheses have been proposed: i) only one false ring can be detected before the first growth ring (winter area), reflecting the transition between the pelagic and the demersal phase (the demersal ring; e.g. Tursi et al. 1994Tursi A., Matarrese A., D’Onghia G., et al. 1994. Population biology of red mullet (Mullus barbatus L.) from the Ionian Sea. Mar. Life 4: 33-43., Sonin et al. 2007Sonin O., Spanier E., Levi D., et al. 2007. Nanism (dwarfism) in fish: a comparison between red mullet Mullus barbatus from the southeastern and the central Mediterranean. Mar. Ecol. Prog. Ser. 343: 221-228., Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.); and ii) two false growth increments can be identified before the first growth ring, the first laid down during the pelagic phase and the second reflecting the settlement (pelagic and demersal rings, respectively; e.g. Vrantzas et al. 1992Vrantzas N., Kalagia M., Karlou C. 1992. Age, growth and state of stock of red mullet (Mullus barbatus L. 1758) in the Saronikos Gulf of Greece. FAO Fish. Rep. 477: 51-67., Fiorentino et al. 1998Fiorentino F., Zamboni A., Rossi M., et al. 1998. The growth of the Red Mullet (Mullus barbatus, L. 1758) during the first years of life in the Ligurian Sea (Mediterranean). CHIEAM-Options Mediterr. 35: 65-78., Sieli et al. 2011Sieli G., Badalucco C., Di Stefano G., et al. 2011, Biology of red mullet, Mullus barbatus (L. 1758), in the Gulf of Castellammare (NW Sicily, MediterraneanSea) subject to a trawling ban. J. Appl. Ichthyol. 27: 1218-1225.). These two hypotheses result in two different growth scenarios: i) the slow-growth hypothesis if only one false ring is detected; and ii) the fast-growth hypothesis if two false rings are detected before the first winter growth increment. A recent study carried out in the southern Adriatic (GSA 18) using marginal analysis, back-calculation and morphological analysis on juveniles of red mullets demonstrated that only one false ring (the demersal ring) is laid down before the first true winter ring (Carbonara et al. 2018Carbonara P., Intini S., Kolitari J., et al. 2018. A holistic approach to the age validation of Mullus barbatus L., 1758 in the Southern Adriatic Sea (Central Mediterranean). Sci. Rep. 8: 13219.), thus supporting the slow-growth hypothesis. Nevertheless, the present results have shown that the number of false rings is less important than reader experience, except for the age group 0. This point, however, may be also linked to the different ageing experience of the readers. Therefore, workshops, age exercises and exchanges are considered fundamental tools for improving precision in red mullet age analysis (ICES 2011bICES. 2011b. Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS), 7 - 11 February 2011, Vienna, Austria. ICES CM 2011/ACOM:40, 174 pp.). Internal and external ageing exercises should be implemented before age data are included in stock assessment, and at least a minimum level of agreement with experienced readers should be achieved (90% PA and 10% CV; ICES 2011bICES. 2011b. Report of the Planning Group on Commercial Catches, Discards and Biological Sampling (PGCCDBS), 7 - 11 February 2011, Vienna, Austria. ICES CM 2011/ACOM:40, 174 pp.).

The results of the present analysis further demonstrate the importance of a handbook to clarify and standardize ageing schemes (e.g. birthdate) and criteria (e.g. number of false rings before the first winter growth increment). The use of a common and standardized protocol among all institutes and experts is fundamental in order to decrease the relative/absolute bias associated with the activities of age determination and to improve the precision (reproducibility and reduction of the CV) of the age readers from the various laboratories involved in the ageing analysis. Furthermore, placing all laboratories under the same standardized protocol can ensure the possibility of applying changes to datasets horizontally when future breakthroughs and/or ground-breaking discoveries are made. All these actions can make an important contribution to overcome ageing uncertainties, thus providing accurate and robust input data for stock assessments and ensuring a more appropriate fishery management of red mullet in the Mediterranean.

ACKNOWLEDGEMENTSTop

The MEDITS scientific surveys are supported by the European Union Data Collection Framework and the Member States. The authors are grateful to the two anonymous reviewers for their constructive comments and suggestions, which greatly helped to improve the manuscript.

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