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	<front>
		<journal-meta>
			<journal-id journal-id-type="publisher-id">SCIENTIA MARINA</journal-id>
			<journal-title-group>
				<journal-title>Scientia Marina</journal-title>
				<abbrev-journal-title>Sci Mar</abbrev-journal-title>
			</journal-title-group>
			<issn pub-type="epub">0214-8358</issn>
			<publisher>
				<publisher-name>Consejo Superior de Investigaciones Científicas</publisher-name>
			</publisher>
		</journal-meta>
		<article-meta>
			 <article-id pub-id-type="publisher-id">sm4988</article-id>
			 <article-id pub-id-type="doi">10.3989/scimar.04988.12A</article-id>
			 
			
		<title-group>
			  <article-title>Spatial distribution pattern of European hake, <italic>Merluccius merluccius</italic> (Pisces: Merlucciidae), in the Mediterranean Sea</article-title>
			<trans-title-group xml:lang="es">
				<trans-title>Patrón de distribución espacial de la merluza europea <italic>Merluccius merluccius</italic> (Pisces: Merlucciidae) en el mar Mediterráneo</trans-title>
			</trans-title-group>
			<alt-title alt-title-type="running-head">Spatial distribution pattern of European hake</alt-title>
		</title-group>
		
			<contrib-group>
			<contrib contrib-type="issue-editor"> 
				<name>
				 <surname>Spedicato</surname>
				 <given-names>M.T.</given-names>
				</name>
				<role>Special Issue Editor</role>
				</contrib>
			<contrib contrib-type="issue-editor"> 
				<name>
				 <surname>Tserpes</surname>
				 <given-names>G.</given-names>
				</name>
				<role>Special Issue Editor</role>
				</contrib>
			<contrib contrib-type="issue-editor"> 
				<name>
				 <surname>Mérigot</surname>
				 <given-names>B.</given-names>
				</name>
				<role>Special Issue Editor</role>
				</contrib>
		<contrib contrib-type="issue-editor"> 
				<name>
				 <surname>Massutí</surname>
				 <given-names>E.</given-names>
				</name>
				<role>Special Issue Editor</role>
				</contrib>
	</contrib-group>

	<contrib-group>
		<contrib contrib-type="author" corresp="yes"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0308-1841</contrib-id>
			<name>
				 <surname>Sion</surname>
				 <given-names>Letizia</given-names>
			</name>
			<xref ref-type="aff" rid="U1"/>
			<ext-link ext-link-type="email" xlink:href="mailto:letizia.sion@uniba.it">letizia.sion@uniba.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2058-8652</contrib-id>
			<name>
				 <surname>Zupa</surname>
				 <given-names>Walter</given-names>
			</name>
			<xref ref-type="aff" rid="U2"/>
			<ext-link ext-link-type="email" xlink:href="mailto:zupa@coispa.eu">zupa@coispa.eu</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2729-215X</contrib-id>
			<name>
				 <surname>Calculli</surname>
				 <given-names>Crescenza</given-names>
			</name>
			<xref ref-type="aff" rid="U3"/>
			<ext-link ext-link-type="email" xlink:href="mailto:crescenza.calculli@uniba.it">crescenza.calculli@uniba.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9117-6252</contrib-id>
			<name>
				 <surname>Garofalo</surname>
				 <given-names>Germana</given-names>
			</name>
			<xref ref-type="aff" rid="U4"/>
			<ext-link ext-link-type="email" xlink:href="mailto:germana.garofalo@irbim.cnr.it">germana.garofalo@irbim.cnr.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3494-9658</contrib-id>
			<name>
				 <surname>Hidalgo</surname>
				 <given-names>Manuel</given-names>
			</name>
			<xref ref-type="aff" rid="U5"/>
			<xref ref-type="aff" rid="U13"/>
			<ext-link ext-link-type="email" xlink:href="mailto:jm.hidalgo@ieo.es">jm.hidalgo@ieo.es</ext-link>
		</contrib>				
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-6858-3570</contrib-id>
			<name>
				 <surname>Jadaud</surname>
				 <given-names>Angélique</given-names>
			</name>
			<xref ref-type="aff" rid="U6"/>
			<ext-link ext-link-type="email" xlink:href="mailto:ajadaud@ifremer.fr">ajadaud@ifremer.fr</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7868-5375</contrib-id>
			<name>
				 <surname>Lefkaditou</surname>
				 <given-names>Evgenia</given-names>
			</name>
			<xref ref-type="aff" rid="U7"/>
			<ext-link ext-link-type="email" xlink:href="mailto:teuthis@hcmr.gr">teuthis@hcmr.gr</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1036-3553</contrib-id>
			<name>
				 <surname>Ligas</surname>
				 <given-names>Alessandro</given-names>
			</name>
			<xref ref-type="aff" rid="U8"/>
			<ext-link ext-link-type="email" xlink:href="mailto:ligas@cibm.it">ligas@cibm.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8608-078X</contrib-id>
			<name>
				 <surname>Peristeraki</surname>
				 <given-names>Panagiota</given-names>
			</name>
			<xref ref-type="aff" rid="U9"/>
			<xref ref-type="aff" rid="U10"/>
			<ext-link ext-link-type="email" xlink:href="mailto:notap@hcmr.gr">notap@hcmr.gr</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8497-1642</contrib-id>
			<name>
				 <surname>Bitetto</surname>
				 <given-names>Isabella</given-names>
			</name>
			<xref ref-type="aff" rid="U2"/>
			<ext-link ext-link-type="email" xlink:href="mailto:bitetto@coispa.it">bitetto@coispa.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1498-0228</contrib-id>
			<name>
				 <surname>Capezzuto</surname>
				 <given-names>Francesca</given-names>
			</name>
			<xref ref-type="aff" rid="U1"/>
			<ext-link ext-link-type="email" xlink:href="mailto:francesca.capezzuto@uniba.it">francesca.capezzuto@uniba.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9287-6936</contrib-id>
			<name>
				 <surname>Carlucci</surname>
				 <given-names>Roberto</given-names>
			</name>
			<xref ref-type="aff" rid="U1"/>
			<ext-link ext-link-type="email" xlink:href="mailto:roberto.carlucci@uniba.it">roberto.carlucci@uniba.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2896-7972</contrib-id>
			<name>
				 <surname>Esteban</surname>
				 <given-names>Antonio</given-names>
			</name>
			<xref ref-type="aff" rid="U11"/>
			<ext-link ext-link-type="email" xlink:href="mailto:antonio.esteban@ieo.es">antonio.esteban@ieo.es</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8320-9974</contrib-id>
			<name>
				 <surname>Follesa</surname>
				 <given-names>Cristina</given-names>
			</name>
			<xref ref-type="aff" rid="U12"/>
			<ext-link ext-link-type="email" xlink:href="mailto:follesac@unica.it">follesac@unica.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2083-4681</contrib-id>
			<name>
				 <surname>Guijarro</surname>
				 <given-names>Beatriz</given-names>
			</name>
			<xref ref-type="aff" rid="U13"/>
			<ext-link ext-link-type="email" xlink:href="mailto:beatriz.guijarro@ieo.es">beatriz.guijarro@ieo.es</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-3157-0500</contrib-id>
			<name>
				 <surname>Ikica</surname>
				 <given-names>Zdravko</given-names>
			</name>
			<xref ref-type="aff" rid="U14"/>
			<ext-link ext-link-type="email" xlink:href="mailto:zdikica@ac.me">zdikica@ac.me</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-7101-9575</contrib-id>
			<name>
				 <surname>Isajlovic</surname>
				 <given-names>Igor</given-names>
			</name>
			<xref ref-type="aff" rid="U15"/>
			<ext-link ext-link-type="email" xlink:href="mailto:igor@izor.hr">igor@izor.hr</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9899-6189</contrib-id>
			<name>
				 <surname>Lembo</surname>
				 <given-names>Giuseppe</given-names>
			</name>
			<xref ref-type="aff" rid="U2"/>
			<ext-link ext-link-type="email" xlink:href="mailto:lembo@coispa.it">lembo@coispa.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2852-4856</contrib-id>
			<name>
				 <surname>Manfredi</surname>
				 <given-names>Chiara</given-names>
			</name>
			<xref ref-type="aff" rid="U16"/>
			<ext-link ext-link-type="email" xlink:href="mailto:chiara.manfredi3@unibo.it">chiara.manfredi3@unibo.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1684-0210</contrib-id>
			<name>
				 <surname>Pérez</surname>
				 <given-names>José Luis</given-names>
			</name>
			<xref ref-type="aff" rid="U13"/>
			<ext-link ext-link-type="email" xlink:href="mailto:joseluis.perez@ieo.es">joseluis.perez@ieo.es</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2649-6502</contrib-id>
			<name>
				 <surname>Porcu</surname>
				 <given-names>Cristina</given-names>
			</name>
			<xref ref-type="aff" rid="U12"/>
			<ext-link ext-link-type="email" xlink:href="mailto:cporcu@unica.it">cporcu@unica.it</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0940-2212</contrib-id>
			<name>
				 <surname>Thasitis</surname>
				 <given-names>Ioannis</given-names>
			</name>
			<xref ref-type="aff" rid="U17"/>
			<ext-link ext-link-type="email" xlink:href="mailto:ithasitis@dfmr.moa.gov.cy">ithasitis@dfmr.moa.gov.cy</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-9052-4091</contrib-id>
			<name>
				 <surname>Tserpes</surname>
				 <given-names>George</given-names>
			</name>
			<xref ref-type="aff" rid="U18"/>
			<ext-link ext-link-type="email" xlink:href="mailto:gtserpes@hcmr.gr">gtserpes@hcmr.gr</ext-link>
		</contrib>
		<contrib contrib-type="author" corresp="no"> 
			<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2529-2535</contrib-id>
			<name>
				 <surname>Carbonara</surname>
				 <given-names>Pierluigi</given-names>
			</name>
			<xref ref-type="aff" rid="U2"/>
			<ext-link ext-link-type="email" xlink:href="mailto:carbonara@coispa.it">carbonara@coispa.it</ext-link>
		</contrib>
			 			 
			  <aff id="U1">Department of Biology, University of Bari Aldo Moro, via Orabona 4, 70125, Italy.</aff>
			  <aff id="U2">COISPA Tecnologia &amp; Ricerca, via dei Trulli 18-20, Bari, Italy.</aff>
			  <aff id="U3">Department of Economics and Finance, University of Bari Aldo Moro, Largo Abbazia S. Scolastica 53, Bari, Italy.</aff>
			  <aff id="U4">Instituto Español de Oceanografía, Centro Oceanográfico de Málaga, Muelle Pesquero s/n, 29640 Fuengirola, Málaga, Spain.</aff>
			  <aff id="U5">Istituto per le Risorse Biologiche e le Biotecnologie Marine, Consiglio Nazionale delle Ricerche, Mazara del Vallo, TP, Italy.</aff>
			  <aff id="U6">MARBEC, Ifremer, Univ Montpellier, CNRS, IRD, Sète, France.</aff>
			  <aff id="U7">Institute of Marine Biological Resources &amp; Inland Waters, Hellenic Centre for Marine Research, 46.5 km Athens-Sounio Ave. Anavyssos, Attica, Greece.</aff>
			  <aff id="U8">Consorzio per il Centro Interuniversitario di Biologia Marina ed Ecologia Applicata, viale N. Sauro 4, 57128 Livorno, Italy.</aff>
			  <aff id="U9">Hellenic Center for Marine Research, Institute of Marine Biological Resources and Inland Waters, 71003 Heraklion, Greece.</aff>
			  <aff id="U10">University of Crete, Biology Department, Stavrakia, Heraklion, Crete.</aff>
			  <aff id="U11">Instituto Español de Oceanografía, Centro Oceanográfico de Murcia, Calle Varadero 1, 30740 San Pedro del Pinatar, Murcia, Spain.</aff>
			  <aff id="U12">Department of Life and Environmental Science, University of Cagliari, Via T. Fiorelli 1, Cagliari, Italy.</aff>
			  <aff id="U13">Instituto Español de Oceanografía, Centro Oceanográfico de les Baleares, Muelle de Poniente s/n, Apdo. 291, 07015 Palma de Mallorca, Spain.</aff>
			  <aff id="U14">Institute of Marine Biology, University of Montenegro, Dobrota b.b., P.O.Box 69, 85330 Kotor, Montenegro.</aff>
			  <aff id="U15">Institute of Oceanography and Fisheries, Set. I Mestrovica 63, 21000 Split, Croatia.</aff>
			  <aff id="U16">Laboratorio di Biologia Marina e Pesca di Fano, Dipartimento BIGEA, Viale Adriatico 1/N Fano (PU), Italy.</aff>
			  <aff id="U17">Department of Fisheries and Marine Research, 101 Vithleem str., 1416 Nicosia, Cyprus.</aff>
			  <aff id="U18">Hellenic Centre for Marine Research, Institute of Marine Biological Resources and Inland Waters, PO Box 2214, 71003 Heraklion, Greece.</aff>
		 </contrib-group>
		 <contrib-group>
			<contrib contrib-type="editor">
				<name>
					<surname>Mérigot</surname>
					<given-names>B.</given-names>
				</name>
				<role>Editor</role>
			</contrib>
		</contrib-group>	 
		
<pub-date pub-type="epub">
		<day>31</day>
		<month>12</month>
		<year>2019</year>
		</pub-date>
		<pub-date pub-type="collection">
		<year>2019</year>
		</pub-date>
		
		<volume>83S1</volume>
		<issue>Suppl. 1</issue>
		<issue-title>
Mediterranean demersal resources and ecosystems: 25 years of MEDITS trawl surveys</issue-title>
		<fpage>21</fpage>
		<lpage>32</lpage>
		
		<elocation-id content-type="doi">10.3989/scimar.04988.12A</elocation-id>

		 <history>
		  	<date date-type="received">
				<day>30</day>
				<month>4</month>
				<year>2019</year>
			</date>
			<date date-type="accepted">
				<day>7</day>
				<month>8</month>
				<year>2019</year>
			</date>
			<date date-type="published">
				<day>25</day>
				<month>10</month>
				<year>2019</year>
			</date>
		 </history>
		 
		<permissions>
		<copyright-statement>&#x00A9; 2019 CSIC</copyright-statement>
		<copyright-year>2019</copyright-year>
				<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/">
		<license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.</license-p>
		</license>
		</permissions>
		
		<abstract xml:lang="en">
		<title>SUMMARY</title>
		<p>The present study provides updated information on the occurrence, abundance and biomass distribution patterns and length frequencies of <italic>Merluccius merluccius</italic> in the Mediterranean Sea, by analysing a time series of data from the Mediterranean International Trawl Surveys (MEDITS) from 1994 to 2015. The highest values of abundance and biomass were observed in the Sardinian Seas. The use of a generalized additive model, in which standardized biomass indices (kg km<sup>–2</sup>) were analysed as a function of environmental variables, explained how ecological factors could affect the spatio-temporal distribution of European hake biomass in the basin. High biomass levels predicted by the model were observed especially at 200 m depth and between 14°C and 18°C, highlighting the preference of the species for colder waters. A strong reduction of biomass was observed since the year 2009, probably due to the strengthening of the seasonal thermocline that had greatly reduced the availability of food. The general decrease in biomass of several stocks of anchovy and sardine, preys of European hake, might be indirectly connected to the decreasing biomass detected in the present study. The length analysis shows median values lower than 200 mm total length of most of the investigated areas.</p>
		</abstract>
		<trans-abstract xml:lang="es">
		<title>RESUMEN</title>
		<p>El presente estudio proporciona información sobre los patrones de ocurrencia, abundancia y distribución de biomasa, y las longitudes de <italic>Merluccius merluccius</italic> en el mar Mediterráneo, considerando el análisis de una serie temporal proveniente de las campañas internacionales de pesca de arrastre en el Mediterráneo (MEDITS) de 1994 a 2015. Los valores más altos de abundancia y biomasa se observaron en los mares de Cerdeña. El uso del modelo GAM, en el que se analizaron los índices estandarizados de biomasa (BI, kg km<sup>–2</sup>) en función de las variables ambientales, permitió explicar cómo los factores ecológicos podrían afectar la distribución espacio-temporal de la biomasa de merluza europea en la cuenca. Los niveles altos de biomasa pronosticados por el modelo se observaron especialmente a 200 m de profundidad y entre 14 y 18°C, destacando la preferencia de la especie por aguas más frías. Se observó una fuerte reducción de la biomasa desde el año 2009, probablemente debido al fortalecimiento de la termoclina estacional que había reducido en gran medida la disponibilidad de alimentos. Otros procesos como la disminución general de la biomasa de varias poblaciones de anchoa y sardina, las presas de merluza europea, podrían estar indirectamente relacionadas con la disminución de la biomasa detectada en el presente estudio. El análisis de longitud muestra valores medios inferiores a 200 mm TL de la mayoría de las áreas investigadas.</p>
		</trans-abstract>
		<kwd-group xml:lang="en">
			<title>KEYWORDS</title>
			<kwd><italic>Merluccius merluccius</italic></kwd>
			<kwd>distribution pattern</kwd>
			<kwd>environmental factors</kwd>
			<kwd>MEDITS</kwd>
			<kwd>Mediterranean Sea</kwd>
		</kwd-group>
		<kwd-group xml:lang="es">
			<title>PALABRAS CLAVE</title>
			<kwd><italic>Merluccius merluccius</italic></kwd>
			<kwd>patrón de distribución</kwd>
			<kwd>factores ambientales</kwd>
			<kwd>MEDITS</kwd>
			<kwd>mar Mediterráneo</kwd>
		</kwd-group>
	 </article-meta>
	</front>	

<body>
<sec id="S1">
<title>INTRODUCTION</title>
			<p>Multispecies fisheries targeting a large number of species represent a management challenge because several biological and ecological parameters must be incorporated in the assessment (<xref ref-type="bibr" rid="CIT12">Ekerhovd and Steinshamn 2016</xref>). This is the case of the Mediterranean ﬁsheries, where the commercial catches are characterized by a high number of target species, among which the most ecologically and economically important one is the European hake, <italic>Merluccius merluccius </italic>(Linnaeus, 1758) (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>).</p>
			<p><italic>M. merluccius</italic> is widely distributed in the Mediterranean Sea and the northeastern Atlantic Ocean, from the coasts of Norway and Iceland to Mauritania. In the Black Sea, it only occurs along the southern coasts. It is a major component of the demersal fish assemblages and can reach depths ranging from 30 to 1000 m, although the most abundant catches are observed especially between 50 and 400 m depth (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>).</p>
  <p>The European hake has always been an important food resource for the population of western Europe throughout history. It is caught in mixed fisheries along with blue whiting, megrim, monkfish, shortfin squid, Norway lobster and deep-water rose shrimp by a multi-rigged fleet. This demersal species is one of the most important commercial resources for trawl and small-scale fisheries (fixed gillnets, bottom longlines) in the Mediterranean basin (<xref ref-type="bibr" rid="CIT41">STECF 2015</xref>). Trawlers mainly catch specimens below 30 cm total length, whereas bottom longlines and gillnets target adults (<xref ref-type="bibr" rid="CIT40">Sbrana et al. 2007</xref>).</p>
			<p>European hake represents about 2% of the average total landings in the Mediterranean Sea (<xref ref-type="bibr" rid="CIT16">FAO 2016</xref>). However, according to the data series, annual catches decreased from a maximum of <italic>ca</italic>. 52000 tonnes in 1994-1995 (<xref ref-type="bibr" rid="CIT31">Maynou et al. 2003</xref>) to 24900 tonnes in the period 2000-2013, indicating an overfishing status in several Mediterranean areas (<xref ref-type="bibr" rid="CIT41">STECF 2015</xref>, <xref ref-type="bibr" rid="CIT19">GFCM 2016</xref>). Several scientists have highlighted the risk of a stock collapse (<xref ref-type="bibr" rid="CIT39">Russo et al. 2017</xref>); indeed, European hake stocks in the Mediterranean Sea suffer the highest fishing pressure, with a fishing mortality rate that is on average five times higher than the target fishing mortality level (<xref ref-type="bibr" rid="CIT16">FAO 2016</xref>).</p>
			<p>Studies on <italic>M. merluccius</italic> have covered aspects such as biology (<xref ref-type="bibr" rid="CIT35">Papaconstantinou and Stergiou 1995</xref>), genetics (<xref ref-type="bibr" rid="CIT28">Levi et al. 2004</xref>), recruitment (<xref ref-type="bibr" rid="CIT17">Fiorentino et al. 2003</xref>, <xref ref-type="bibr" rid="CIT25">Hidalgo et al. 2008</xref>, <xref ref-type="bibr" rid="CIT26">2009</xref>), nurseries modelling (<xref ref-type="bibr" rid="CIT07">Carlucci et al. 2009</xref>, <xref ref-type="bibr" rid="CIT11">Druon et. al. 2015</xref>), reproduction (<xref ref-type="bibr" rid="CIT01">Al-Absawy 2010</xref>), age and growth (<xref ref-type="bibr" rid="CIT29">Ligas et al. 2015</xref>, <xref ref-type="bibr" rid="CIT36">Pattoura et al. 2015</xref>), feeding (<xref ref-type="bibr" rid="CIT08">Carpentieri et al. 2005</xref>, <xref ref-type="bibr" rid="CIT32">Mellon-Duval et al. 2017</xref>), and exploitation and fishery management (<xref ref-type="bibr" rid="CIT39">Russo et al. 2017</xref>, <xref ref-type="bibr" rid="CIT45">Tsagarakis et al. 2017</xref>). These studies on the Mediterranean Sea are regional but most contributions have a local focus.</p>
			<p>Since 1994, the MEDITS project (<xref ref-type="bibr" rid="CIT05">Bertrand et al. 2002</xref>) has provided the opportunity to collect biological and abundance data on demersal species at a Mediterranean Basin scale, by means of large-scale experimental trawl surveys and standardized sampling protocols. In this context, <xref ref-type="bibr" rid="CIT33">Orsi Relini et al. (2002)</xref> presented data (1994-1999) on abundance, population structure and recruitment pattern of European hake from the Alboran Sea to the Aegean Sea, providing the first integrative picture of European hake over the northern Mediterranean Sea.</p>
		  <p>The present study aims to provide an update of previous studies, presenting more advanced analyses on several aspects of this species, in terms of both methodological approach and time period of investigation, also analysing several drivers shaping its distribution. Considering MEDITS time series from 1994 to 2015, updated data on occurrence, abundance patterns and mean length of <italic>M. merluccius</italic> in the northern Mediterranean Sea are presented. Finally, the spatial distribution of biomass of this demersal species as a function of environmental variables was analysed and a predictive map of biomass hotspots was obtained.</p>
		</sec>
<sec id="S2">
<title>MATERIALS AND METHODS</title>
<sec id="S2.1">
<title>Sampling</title>
  <p>Since 1994, twenty-two annual bottom trawl surveys have been performed covering the European Union Mediterranean Sea in the framework of the MEDITS project. These surveys mainly aim to obtain estimates of abundance indices and length frequency distributions for a set of target species and have usually been carried out from late spring to mid-summer on demersal grounds from 10 to 800 m depth. A stratified random sampling design was adopted considering five bathymetric strata: 10-50 m, 51-100 m, 101-200 m, 201-500 m and 501-800 m. Details of the survey design and sampling methodology can be found in <xref ref-type="bibr" rid="CIT05">Bertrand et al. (2002)</xref> and in the MEDITS Handbook (<xref ref-type="bibr" rid="CIT03">Anonymous 2017</xref>).</p>
			<p>In the present study, catch data (number and weight) and total length measurements of <italic>M. merluccius</italic> collected in the period 1994-2015 from 16 geographical sub-areas (GSAs) (<xref ref-type="bibr" rid="CIT14">FAO 2009</xref>) belonging to nine countries (<xref ref-type="table" rid="T1">Table 1</xref>, <xref ref-type="fig" rid="F1">Fig. 1</xref>) were analysed. In GSAs 5, 17, 20, 22 and 23, the time series are not continuous because MEDITS surveys were not performed for some years; however, they were included in the analysis in order to cover the largest geographical patterns possible.</p>
				<table-wrap id="T1">
			<label>Table 1</label>
		<caption>
			<title>List of the GSAs involved in the analysis.</title>
		</caption>
		<table frame="hsides" rules="groups">
  <thead>
			      <tr>
			        <th> GSA </th>
			        <th> Country </th>
			        <th> Area </th>
		          </tr>
		        </thead>
			    <tbody>
			      <tr>
			        <td> 1 </td>
			        <td> Spain </td>
			        <td> Northern Alboran Sea </td>
		          </tr>
			      <tr>
			        <td> 5 </td>
			        <td> Spain </td>
			        <td> Balearic Islands </td>
		          </tr>
			      <tr>
			        <td> 6 </td>
			        <td> Spain </td>
			        <td> Northern Spain </td>
		          </tr>
			      <tr>
			        <td> 7 </td>
			        <td> France </td>
			        <td> Gulf of Lions </td>
		          </tr>
			      <tr>
			        <td> 8 </td>
			        <td> France </td>
			        <td> Corsica </td>
		          </tr>
			      <tr>
			        <td> 9 </td>
			        <td> Italy </td>
			        <td> Ligurian, northern and central Tyrrhenian seas </td>
		          </tr>
			      <tr>
			        <td> 10 </td>
			        <td> Italy </td>
			        <td> Central and southern Tyrrhenian Sea </td>
		          </tr>
			      <tr>
			        <td> 11 </td>
			        <td> Italy </td>
			        <td> Sardinian Seas </td>
		          </tr>
			      <tr>
			        <td> 16 </td>
			        <td> Italy </td>
			        <td> South of Sicily </td>
		          </tr>
			      <tr>
			        <td> 17 </td>
			        <td> Italy, Slovenia, Croatia </td>
			        <td> Northern Adriatic Sea </td>
		          </tr>
			      <tr>
			        <td> 18 </td>
			        <td> Italy, Albania, Montenegro </td>
			        <td> Southern Adriatic Sea </td>
		          </tr>
			      <tr>
			        <td> 19 </td>
			        <td> Italy </td>
			        <td> Northwestern Ionian Sea </td>
		          </tr>
			      <tr>
			        <td> 20 </td>
			        <td> Greece </td>
			        <td> Eastern Ionian Sea </td>
		          </tr>
			      <tr>
			        <td> 22 </td>
			        <td> Greece </td>
			        <td> Argosaronikos, northern and southern Aegean Sea </td>
		          </tr>
			      <tr>
			        <td> 23 </td>
			        <td> Greece </td>
			        <td> Cretan Sea </td>
		          </tr>
			      <tr>
			        <td> 25 </td>
			        <td> Cyprus </td>
			        <td> Cyprus </td>
		          </tr>
		        </tbody>
		      </table>
  </table-wrap>
  			<fig id="F1">
				<label>Fig. 1</label>
				<caption>
				<title>Map of the study area showing the GSAs involved in the MEDITS project with indication of the number of GSAs and the main isobaths.</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig1.jpg"/>
			</fig>

<p>A total of 23748 valid hauls were carried out during the MEDITS surveys (1994-2015); for each haul, number of specimens and total weight (g) per species were collected. For a list of target species, including European hake, biological sampling consisted in recording size (total length, TL in mm), gonad weight (g), sex and gonad maturation stage.</p>
		</sec>
<sec id="S2.2">
<title>Data analysis</title>
  <p>The frequency of occurrence of European hake (F(%)=N positive hauls/total hauls×100) was computed for each GSA considering the overall depth range (10-800 m). The number of individuals (N) and weight (kg) were standardized to a trawled surface unit (km<sup>2</sup>) as abundance (N km<sup>–2</sup>) and biomass (kg km<sup>–2</sup>) indices. Net horizontal opening and recorded distances were used to calculate the swept area. Their distributions were represented graphically for each GSA by means of box plots showing medians, quartiles and ranges (maximum and minimum).</p>
			<p>One-way analysis of variance (ANOVA) (<xref ref-type="bibr" rid="CIT49">Yandell 1997</xref>) was performed on log- transformed abundance (N km<sup>–2</sup>) and biomass (kg km<sup>–2</sup>), followed by pair-wise post hoc Tukey honestly significant difference tests to investigate for significant differences in terms of abundance and biomass values of <italic>M. merluccius</italic> among GSAs. Normality assumption of abundance and biomass distributions were previously evaluated using the Shapiro-Wilk test.</p>
			<p>The biomass indices (BI, kg km<sup>–2</sup>) were modelled using generalized additive models (GAMs), which provides a flexible framework for modelling the relationship between the individual predictors and the dependent variable through smooth functions that can be both linear and nonlinear (<xref ref-type="bibr" rid="CIT48">Wood 2017</xref>).</p>
			<p>The explanatory variables considered in the model were the geographic coordinates (latitude and longitude) combined in a bidimensional smoother, the mean haul depth (m), sea surface temperature (°C, SST), temperature at the sea bottom (°C, botT) and the surface chlorophyll concentration (mg m<sup>–3</sup>, CHL). These variables were selected for the GAM models because they showed considerable variability throughout the study area and were believed to be easier to interpret biologically than some other variables that were available.</p>
			<p>The year of the surveys was also included as a factor. Depth grid was obtained from the EMODnet Bathymetry portal (<ext-link ext-link-type="uri" xlink:href="http://www.emodnet.eu/">http://www.emodnet.eu/</ext-link>), while data of the other covariates (SST, botT and CHL) were extracted from Mediterranean Monitoring and Forecasting Centre products available on the Copernicus Marine Environment Monitoring Service (CMEMS) web site (<ext-link ext-link-type="uri" xlink:href="http://marine.copernicus.eu">http://marine.copernicus.eu</ext-link>). In particular, SST, and botT data were produced using the physical reanalysis component of the Mediterranean Forecasting System (<xref ref-type="bibr" rid="CIT42">Simoncelli et al. 2014</xref>), while CHL data were produced using the MedBFM biogeochemical model (<xref ref-type="bibr" rid="CIT44">Teruzzi et al. 2014</xref>).</p>
			<p>Absence of collinearity among covariates was checked by means of variance inflation factor (VIF) values (<xref ref-type="bibr" rid="CIT51">Zuur et al. 2010</xref>). Only variables with a low Pearson correlation coefficient (r) (|r|&lt;0.5) and a low VIF value (VIF&lt;3) were considered not correlated.</p>
			<p>BIs were modelled using Gaussian distribution with a logarithmic link function, according to the following specification:</p>
		  <p align="center">log (<italic>biomass</italic>) = α + <italic>s</italic><sub>1</sub>(lon, lat) + <italic>s</italic><sub>2</sub>(depth) + <italic>s</italic><sub>3</sub>(SST) + <italic>s</italic><sub>4</sub>(botT) + <italic>s</italic><sub>5</sub>(SAL) + <italic>s</italic><sub>6</sub>(CHL) + <italic>factor</italic>(year)</p>
  <p>where α represents the intercept, <italic>s</italic><sub>i</sub> (<italic>i</italic>=1..., 6) are the smooth functions of the covariates and <italic>factor</italic> is the step function for the categorical variable year. It is worth noting that, in the model, the smooth function of spatial coordinates represents a residual spatial variation, which is spatially structured. The selection of the best model was realized by minimizing the Akaike information criterion value with the introduction of each variable in the analysis.</p>
			<p>Residuals of the model were tested with the Anderson-Darling normality test (<xref ref-type="bibr" rid="CIT22">Gross and Ligges 2015</xref>), and the symmetry was assessed by estimating the skewness index of the distribution (<xref ref-type="bibr" rid="CIT23">Hair et al. 2010</xref>). The Spearman rho correlation coefficient was carried out to test the presence of trends in residuals.</p>
			<p>For predictions, a 0.03×0.03° resolution grid was used. The bathymetrical extension of the grid was within 10 to 800 m depth. The prediction grid was used to sample the informative layers of the environmental variables (monthly data) used in the model. The grid’s points were also coupled with year and coordinates variables in order to estimate the annual predictive maps throughout the GAM model. In particular, the most representative predictive maps, for the years 1995, 2006, 2011, 2013 and 2015, were chosen to represent biomass values (kg km<sup>–2</sup>) of European hake, and to check the model robustness in space, the annual predictions of the model were coupled with the estimation of the mean stratified BIs per GSA obtained following the mean stratified standardization formulas by <xref ref-type="bibr" rid="CIT43">Souplet (1996)</xref>.</p>
			<p>In order to identify the hotspots of the European hake biomass distribution pattern, prediction maps for the last three years (2013-2015) of the time series were averaged in a single map. This raster map was used to extract the 80<sup>th</sup>, 90<sup>th</sup> and 95<sup>th</sup> percentiles of distribution to draw the actual hotspot contours.</p>
			<p>Statistical analyses were performed using R version 3.5.2 (<xref ref-type="bibr" rid="CIT38">R Development Core Team 2018</xref>). In particular, GAMs were implemented by means of the <italic>mgcv</italic> R package (<xref ref-type="bibr" rid="CIT48">Wood 2017</xref>). Biomass maps were processed and produced with QGIS software (<xref ref-type="bibr" rid="CIT37">QGIS Development Team 2017</xref>).</p>
		  <p>For each GSA, the length frequencies of <italic>M. merluccius</italic> were graphically represented by means of box plots of median, quartile, minimum and maximum TLs. An ANOVA test was applied to investigate significant differences among GSAs in terms of size distribution.</p>
</sec>
</sec>
<sec id="S3">
<title>RESULTS</title>
			<p>A total of 1880472 <italic>M. merluccius</italic> individuals were caught in 18470 hauls of the 23748 valid hauls (F=77.8%) carried out in the overall depth range (10–800 m) during the MEDITS surveys (1994- 2015) (<xref ref-type="table" rid="T2">Table 2</xref>). The highest frequency of occurrence was recorded in the Gulf of Lions (F=93.6%) and the southern Adriatic Sea (F=90.4%), followed by the northern Adriatic ((F=87.1%), the eastern Ionian Sea (F=85.0%), the Aegean Sea (F=83.4%) and northern Spain (F=81.2%). The highest maximum abundance (N km<sup>–</sup><sup>2</sup>) and biomass (kg km<sup>–</sup><sup>2</sup>) indices were observed in Sardinian waters (180493 N km<sup>–</sup><sup>2</sup>; 4636 kg km<sup>–</sup><sup>2</sup>) and in the Aegean Sea (129668 N km<sup>–</sup><sup>2</sup>; 2407 kg km<sup>–</sup><sup>2</sup>). Significant catches were also reported in the Ligurian and northern central Tyrrhenian seas (124022 N km<sup>–</sup><sup>2</sup>; 1509 kg km<sup>–</sup><sup>2</sup>), as well as in the Gulf of Lions (102741 N km<sup>–</sup><sup>2</sup>; 1588 kg km<sup>–</sup><sup>2</sup>). The box plots of the log–transformed abundance index (N km<sup>–</sup><sup>2</sup>) suggested a southwest–northeast increase in the median values in the western areas, from the northern Alboran Sea to the Gulf of Lions (GSAs 1, 5, 6, 7), while in central and eastern Mediterranean areas, the median values remained rather similar (<xref ref-type="fig" rid="F2">Fig. 2A</xref>). The highest median abundance values were found in the Gulf of Lions (1210 N km<sup>–</sup><sup>2</sup>) and in northern Spain (1092 N km<sup>–</sup><sup>2</sup>). The box plots of log–transformed BIs (kg km<sup>–</sup><sup>2</sup>) (<xref ref-type="fig" rid="F2">Fig. 2B</xref>) showed stable median values in the entire investigated area; the highest median biomass value was detected in Sardinian waters (31.83 kg km<sup>–</sup><sup>2</sup>). The results of the ANOVA (<xref ref-type="table" rid="T3">Tables 3</xref> and <xref ref-type="table" rid="T4">4</xref>) indicated significant differences among GSAs, in terms of both abundance (F=123.5, p&lt;0.001) and biomass (F=54.98, p&lt;0.001). Furthermore, the post hoc Tukey test performed after the ANOVA indicated significant differences between most pairs of GSAs. No collinearity was detected among covariates, the VIF values being lower than 3 and none of the Pearson correlation coefficients greater than 0.5; therefore, all the covariates were included in the GAM model analysis.</p>
				<table-wrap id="T2">
			<label>Table 2</label>
		<caption>
			<title>Number of MEDITS surveys carried out in the period 1994-2015 and total number of positive hauls, depth range (m), frequency of occurrence (F, %), maximum abundance (N km<sup>-2</sup>) and biomass indices (kg km<sup>-2</sup>) of <italic>M. merluccius</italic> for each GSA involved in the analysis.</title>
		</caption>
		<table frame="hsides" rules="groups">
  <thead>
			      <tr>
			        <th> GSA </th>
			        <th> No. surveys </th>
			        <th> No. positive hauls </th>
			        <th> Depth range (m) </th>
			        <th> F (%) </th>
			        <th> Max. abundance (N km<sup>–2</sup>) </th>
			        <th> Max. biomass (kg km<sup>–2</sup>) </th>
		          </tr>
		        </thead>
			    <tbody>
			      <tr>
			        <td> GSA 1 </td>
			        <td> 22 </td>
			        <td> 464 </td>
			        <td> 38-720 </td>
			        <td> 54.72 </td>
			        <td> 19454 </td>
			        <td> 268 </td>
		          </tr>
			      <tr>
			        <td> GSA 5 </td>
			        <td> 15 </td>
			        <td> 496 </td>
			        <td> 44-747 </td>
			        <td> 60.78 </td>
			        <td> 26325 </td>
			        <td> 692 </td>
		          </tr>
			      <tr>
			        <td> GSA 6 </td>
			        <td> 22 </td>
			        <td> 1378 </td>
			        <td> 34-761 </td>
			        <td> 81.15 </td>
			        <td> 72707 </td>
			        <td> 956 </td>
		          </tr>
			      <tr>
			        <td> GSA 7 </td>
			        <td> 22 </td>
			        <td> 1345 </td>
			        <td> 14-800 </td>
			        <td> 93.60 </td>
			        <td> 102741 </td>
			        <td> 1588 </td>
		          </tr>
			      <tr>
			        <td> GSA 8 </td>
			        <td> 21 </td>
			        <td> 326 </td>
			        <td> 67-571 </td>
			        <td> 71.02 </td>
			        <td> 2675 </td>
			        <td> 176 </td>
		          </tr>
			      <tr>
			        <td> GSA 9 </td>
			        <td> 22 </td>
			        <td> 2294 </td>
			        <td> 12-757 </td>
			        <td> 78.00 </td>
			        <td> 124022 </td>
			        <td> 1509 </td>
		          </tr>
			      <tr>
			        <td> GSA 10 </td>
			        <td> 22 </td>
			        <td> 1129 </td>
			        <td> 15-663 </td>
			        <td> 68.14 </td>
			        <td> 52626 </td>
			        <td> 634 </td>
		          </tr>
			      <tr>
			        <td> GSA 11 </td>
			        <td> 22 </td>
			        <td> 1744 </td>
			        <td> 17-679 </td>
			        <td> 74.28 </td>
			        <td> 180493 </td>
			        <td> 4636 </td>
		          </tr>
			      <tr>
			        <td> GSA 16 </td>
			        <td> 22 </td>
			        <td> 1357 </td>
			        <td> 19-781 </td>
			        <td> 77.06 </td>
			        <td> 34545 </td>
			        <td> 706 </td>
		          </tr>
			      <tr>
			        <td> GSA 17 </td>
			        <td> 22 </td>
			        <td> 3154 </td>
			        <td> 13-733 </td>
			        <td> 87.10 </td>
			        <td> 27984 </td>
			        <td> 580 </td>
		          </tr>
			      <tr>
			        <td> GSA 18 </td>
			        <td> 22 </td>
			        <td> 1877 </td>
			        <td> 14-667 </td>
			        <td> 90.41 </td>
			        <td> 12444 </td>
			        <td> 230 </td>
		          </tr>
			      <tr>
			        <td> GSA19 </td>
			        <td> 22 </td>
			        <td> 924 </td>
			        <td> 20-772 </td>
			        <td> 58.82 </td>
			        <td> 34682 </td>
			        <td> 610 </td>
		          </tr>
			      <tr>
			        <td> GSA 20 </td>
			        <td> 14 </td>
			        <td> 322 </td>
			        <td> 34-724 </td>
			        <td> 84.96 </td>
			        <td> 49739 </td>
			        <td> 1246 </td>
		          </tr>
			      <tr>
			        <td> GSA 22 </td>
			        <td> 14 </td>
			        <td> 1403 </td>
			        <td> 20-762 </td>
			        <td> 83.36 </td>
			        <td> 129668 </td>
			        <td> 2407 </td>
		          </tr>
			      <tr>
			        <td> GSA 23 </td>
			        <td> 14 </td>
			        <td> 157 </td>
			        <td> 58-591 </td>
			        <td> 67.97 </td>
			        <td> 27724 </td>
			        <td> 403 </td>
		          </tr>
			      <tr>
			        <td> GSA 25 </td>
			        <td> 10 </td>
			        <td> 100 </td>
			        <td> 72-619 </td>
			        <td> 38.61 </td>
			        <td> 341 </td>
			        <td> 38 </td>
		          </tr>
		        </tbody>
		      </table>
  </table-wrap>
  			<fig id="F2">
				<label>Fig. 2</label>
				<caption>
				<title>Box plot of log-transformed abundance (A; N km<sup>–2</sup>), and biomass (B; kg km<sup>–2</sup>) indices by GSA. The boxes range from the first to the third quartile, the central line is the median, and the whiskers range from minimum to maximum values.</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig2.jpg"/>
			</fig>

	<table-wrap id="T3">
			<label>Table 3</label>
		<caption>
			<title>Significance level of the GSA effect on the abundance index from Tukey post hoc tests after one-way ANOVA (F=123.5, p&lt;0.001); * p&lt;0.01.</title>
		</caption>
		<table frame="hsides" rules="groups">
  <thead>
        <tr>
          <th> GSA </th>
          <th> 1 </th>
          <th> 5 </th>
          <th> 6 </th>
          <th> 7 </th>
          <th> 8 </th>
          <th> 9 </th>
          <th> 10 </th>
          <th> 11 </th>
          <th> 16 </th>
          <th> 17 </th>
          <th> 18 </th>
          <th> 19 </th>
          <th> 20 </th>
          <th> 22 </th>
          <th> 23 </th>
          <th> 25 </th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th> 1 </th>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 5 </th>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 6 </th>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
        </tr>
        <tr>
          <th> 7 </th>
          <td> * </td>
          <td> * </td>
          <td />          
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 8 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 9 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 10 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 11 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 16 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 17 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 18 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 19 </th>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 20 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 22 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td></td>
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 23 </th>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th> 25 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
        </tr>
      </tbody>
    </table>
  </table-wrap>
  	<table-wrap id="T4">
			<label>Table 4</label>
		<caption>
			<title>Significance level of the GSA effect on the biomass index from Tukey post hoc tests after one-way ANOVA (F=54.98, p&lt;0.001); * p&lt;0.01.</title>
		</caption>
		<table frame="hsides" rules="groups">
  <thead>
        <tr>
          <th> GSA </th>
          <th> 1 </th>
          <th> 5 </th>
          <th> 6 </th>
          <th> 7 </th>
          <th> 8 </th>
          <th> 9 </th>
          <th> 10 </th>
          <th> 11 </th>
          <th> 16 </th>
          <th> 17 </th>
          <th> 18 </th>
          <th> 19 </th>
          <th> 20 </th>
          <th> 22 </th>
          <th> 23 </th>
          <th> 25 </th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th> 1 </th>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 5 </th>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 6 </th>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
        </tr>
        <tr>
          <th> 7 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 8 </th>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 9 </th>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 10 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 11 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 16 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 17 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 18 </th>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 19 </th>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td></td>
          <td />          
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 20 </th>
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td />          
          <td />          
          <td />          
          <td />          
          <td></td>
          <td />          
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 22 </th>
          <td> * </td>
          <td />          
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td />          
          <td> * </td>
          <td> * </td>
          <td />          
          <td></td>
          <td />          
          <td></td>
        </tr>
        <tr>
          <th> 23 </th>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td />          
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
          <td></td>
        </tr>
        <tr>
          <th> 25 </th>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td> * </td>
          <td></td>
        </tr>
      </tbody>
    </table>
  </table-wrap>
  <p>The GAM model of biomass explained 82.2% of the variability in the data (deviance explained). All the smoothing terms were significant (p&lt;0.05). The degrees of freedom estimated for all smoothers are reported in <xref ref-type="fig" rid="F3">Figure 3</xref>.</p>
  			<fig id="F3">
				<label>Fig. 3</label>
				<caption>
				<title>Smoothers of the covariates estimated by the GAM model fitted on the biomass index (kg km<sup>–2</sup>).</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig3.jpg"/>
			</fig>

<p>Although residuals of the model tested with the Anderson-Darling normality test (p&lt;0.05) were not normally distributed, the model was considered sufficiently symmetric because its skewness index (0.023) was within the ±1 range. Moreover, the Spearman rho test carried out on the model’s residuals found no significant trend (p&gt;0.05).</p>
			<p>Plots of the smoothers and diagnostic plots of the residuals resulting from the model are presented in <xref ref-type="fig" rid="F3">Figures 3</xref> and <xref ref-type="fig" rid="F4">4</xref>. The model indicated that high BI levels of European hake were mainly detected in the northern Spain area, in the Gulf of Lions, on the western side of Sardinia, in the northern Adriatic Sea and in the northern area of the Aegean Sea. The highest biomass of European hake was identified by the model at 200 m depth, although the optimal depth range can extend up to 400 m depth. In relation to sea surface temperature, fairly stable biomass values were observed between 20°C and 22°C. Considering the bottom temperature’s spline shape, the partial effect of the variable shows an increase in the response variable corresponding to temperatures of between 14°C and 18°C, while a progressive reduction of the biomass was expected at higher bottom temperatures. Also, chlorophyll concentration was linked with European hake presence: the partial effect of the CHL variable, as is shown by the spline shape, describes an increase in biomass at chlorophyll concentrations higher than 0.6 mg m<sup>–</sup><sup>3</sup>, and—albeit for few values—another peak was also evident around higher values than 1.7 mg m<sup>–</sup><sup>3</sup>. The biomass index showed a fairly stable level over time up to 2002 and large fluctuations thereafter. In particular, an increase of the index recording the highest predicted values was observed in the years 2005, 2006 and 2008 for all GSAs. After this period, a reduction of the BI index was observed, with a relative peak in 2013 and the lowest values in 2011. In relation to the year’s effect (<xref ref-type="fig" rid="F3">Fig. 3</xref>), the most representative predictive maps for the years 1995 (start, medium level), 2006 (high level), 2011 (low level), 2013 (high and recent) and 2015 (low and recent) are shown in <xref ref-type="fig" rid="F5">Figure 5</xref>. For the same years, the observed biomass values from MEDITS data were compared with the predictions of the model; comparable tendencies of biomass values were observed for the different GSAs (<xref ref-type="fig" rid="F5">Fig. 5</xref>).</p>
						<fig id="F4">
				<label>Fig. 4</label>
				<caption>
				<title>Diagnostic plot of the residuals of the GAM model fitted on the biomass index (kg km<sup>–2</sup>).</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig4.jpg"/>
			</fig>

			<fig id="F5">
				<label>Fig. 5</label>
				<caption>
				<title>Predictive maps of European hake biomass (kg km<sup>–2</sup>) compared with observed biomass values (kg km<sup>–2</sup>) for the years 1995, 2006, 2011, 2013 and 2015.</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig5.jpg"/>
			</fig>
  <p>The areas with the greatest abundance of European hake were located in the Gulf of Lions, the eastern Adriatic Sea, the western side of Sardinia, the south of Sicily and the northern Aegean Sea. This distribution pattern was confirmed and better evidenced by the map showing the biomass hotspots of the European hake for the years 2013-2015 (<xref ref-type="fig" rid="F6">Fig. 6</xref>) calculated at the 80<sup>th</sup>, 90<sup>th</sup> and 95<sup>th</sup> percentiles of the BI distribution.</p>
  			<fig id="F6">
				<label>Fig. 6</label>
				<caption>
				<title>Biomass hotspots of European hake based on predictive maps averaged over the years 2013-2015.</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig6.jpg"/>
			</fig>

<p>The length frequencies of <italic>M. merluccius</italic> are summarized in the box plots shown in <xref ref-type="fig" rid="F7">Figure 7</xref>. In the western Mediterranean, the largest median value was found in Corsica (230 mm TL), while the smallest median values were detected in northern Spain (125 mm TL) and the Gulf of Lions (130 mm TL). Median values lower than 200 mm TL were also observed in the northern Alboran Sea and in the Balearic Islands. In the central Mediterranean, the median values were below 200 mm TL in all areas (the Ligurian and Tyrrhenian seas, Sardinian waters, south of Sicily, the Adriatic Sea and the northwestern Ionian Sea). A prevalence of median values larger than 200 mm TL was observed in the eastern Ionian Sea except the Cretan Sea (160 mm TL). ANOVA results also suggested a significant difference among GSAs in terms of median size (F=1684, p&lt;0.001).</p>
			<fig id="F7">
				<label>Fig. 7</label>
				<caption>
				<title>Box plot of length frequencies of European hake by GSA. The boxes range from the first to the third quartile, the central line is the median, and the whiskers range from minimum to maximum values.</title>
				</caption>
				<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="../sm83s1-4988-web-resources/image/sm4988fig7.jpg"/>
			</fig>

</sec>
<sec id="S4">
<title>DISCUSSION</title>
  <p>The present study provides updated information on the occurrence, abundance and biomass distribution patterns and length frequencies of <italic>M. merluccius</italic> in the Mediterranean Sea on a large spatial scale during spring-summer over the last two decades. In particular, the use of a GAM model, in which standardized indices of biomass were analysed in relation to the effect of environmental variables, explained how ecological factors could affect the spatio-temporal distribution of European hake biomass in the area.</p>
			<p>The results confirm the wide spatial distribution of the species, in terms of both bathymetric range and occurrence, in accordance with previous studies in several Mediterranean areas (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT27">Katsanevakis et al. 2009</xref>). The significant differences among GSAs in terms of both abundance and biomass highlight a high variability in the catches of this demersal species in the whole area, in agreement with the findings by <xref ref-type="bibr" rid="CIT33">Orsi Relini et al. (2002)</xref>.</p>
			<p>All environmental factors were significant drivers affecting European hake distribution. The application of the GAM model, which considers the effect of some environmental variables, has significantly improved knowledge of the spatio-temporal variations of <italic>M. merluccius</italic> in the Mediterranean Sea. The high biomass levels predicted by the model in northern Spain (the Catalan Sea), the Gulf of Lions, the western side of Sardinia, south of Sicily, the eastern Adriatic Sea (mainly Croatian waters) and the northern Aegean Sea are in accordance with previous studies carried out in the Mediterranean Sea (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT25">Hidalgo et al. 2008</xref>, <xref ref-type="bibr" rid="CIT11">Druon et al. 2015</xref>). Furthermore, the predictive model showed a biomass peak at 200 m depth, most likely correlated to the presence of nursery grounds in many Mediterranean areas (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT11">Druon et al. 2015</xref>). The optimal depth range for BI was observed between 200 and 400 m, beyond which a consistent decrease in biomass was evident throughout the time period. These results are in agreement with previous studies using the MEDITS protocol, which indicate that abundance of European hake increases especially at depths ranging from 100 to 400 m, but decreases at depths greater than 500 m (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT27">Katsanevakis et al. 2009</xref>, <xref ref-type="bibr" rid="CIT18">Garofalo et al. 2018</xref>). This demersal species shows an optimal bottom temperature range of between 14°C and 18°C, with noticeably lower biomass for higher bottom temperatures. The GAM model confirms the results obtained by <xref ref-type="bibr" rid="CIT10">De Pontual et al. (2013)</xref> concerning the preference of the species for cold waters, as it has a limited tolerance to warm temperature.</p>
			<p>The CHL covariate also has a significant effect: an increase in biomass occurs with increasing CHL concentration, which might be considered a proxy of primary production, thus indicating areas where favourable feeding conditions would attract the species (<xref ref-type="bibr" rid="CIT06">Brosset et al. 2017</xref>). <italic>M. merluccius</italic> is an opportunistic feeder (<xref ref-type="bibr" rid="CIT25">Hidalgo et al. 2008</xref>) that is able to feed on a wide range of trophic resources. In the central Mediterranean, <xref ref-type="bibr" rid="CIT08">Carpentieri et al. (2005)</xref> reported that European hake size classes are differentiated along food niche dimensions according to different prey sizes or different prey taxa. During its early demersal life, the species feeds on crustaceans (mostly euphausiids and mysids); subsequently the juvenile hakes migrate from the nursery areas to the parental stock, and when they reach between 18 and 32 cm TL, they shift their diet towards pelagic and necto-benthic fish (<xref ref-type="bibr" rid="CIT09">Carrozzi et al. 2019</xref>). The predicted biomass hotspots of the European hake could be linked to areas characterized by high availability of crustaceans and small pelagic fish consumed mostly by individuals of age 0 and large specimens of European hake respectively.</p>
			<p>This study highlights that the effect of environmental factors such as the range of preferred bottom temperature (14°C-18°C) and the increase in chlorophyll concentration could offer favourable conditions for a productive habitat, enhancing the prey availability and consequently increasing the biomass levels of European hake in Mediterranean areas. The results confirm the potential distribution of juvenile European hake habitats reported by <xref ref-type="bibr" rid="CIT11">Druon et al. (2015)</xref>, with the highest biomass levels of <italic>M. merluccius </italic>occurring in nursery areas located near the shelf break in the northern Catalan Sea, the eastern Gulf of Lions, southwestern Sardinia, the northern Tyrrhenian Sea, southeastern Sicily, the semi-enclosed part of the eastern Ionian Sea and fragmented areas of the Aegean Sea. The prediction of both important nursery habitats (<xref ref-type="bibr" rid="CIT11">Druon et al. 2015</xref>) and biomass hotspots in the northern Aegean Sea is not revealed by MEDITS survey data (<xref ref-type="bibr" rid="CIT46">Tserpes et al. 2008</xref>). The low presence of juveniles in the northern Aegean is rather due to a time lag between the MEDITS sampling period and the seasonal recruitment peak in autumn-winter (Hellenic Centre for Marine Research, unpublished data). The high biomass observed in the present study may confirm the presence of a nursery area in the southern Adriatic Sea (Pomo Pit), as hypothesized by <xref ref-type="bibr" rid="CIT04">Arneri and Morales-Nin (2000)</xref>. The high biomass of small pelagic fish in the Mediterranean basin (<xref ref-type="bibr" rid="CIT34">Palomera et al. 2007</xref>, <xref ref-type="bibr" rid="CIT21">Giannoulaki et al. 2011</xref>) offers a great lure of food for the species. In the southern-central Mediterranean Sea, <xref ref-type="bibr" rid="CIT18">Garofalo et al. (2018)</xref> reported that high small pelagic fish biomass related to the high primary production had led to the presence of large adult European hakes in the Gulf of Gabes. The strong relationship between productive fronts and demersal resources especially linked to fish preys of low trophic level has been observed in other marine areas (<xref ref-type="bibr" rid="CIT02">Alemany et al. 2014</xref>).</p>
			<p>The predictive maps showing the European hake biomass index along a temporal scale highlighted a strong reduction of biomass since the year 2009 according to <xref ref-type="bibr" rid="CIT16">FAO (2016)</xref>, which reports a decrease in European hake landings in Spain, Italy and France since 2009. Also, in the ecological niche modelling developed by <xref ref-type="bibr" rid="CIT11">Druon et al. (2015)</xref> on a Mediterranean scale, a substantial decrease in biomass of recruits and adults in 2010 and 2011 was detected, probably due to the strengthening of the seasonal thermocline that had greatly reduced the availability of food. Also, a recent report by the <xref ref-type="bibr" rid="CIT20">GFCM (2019)</xref> highlights that the increase in sea bottom temperature could likely be responsible for the decrease in hake nurseries in recent years.</p>
			<p>Environmental factors such as temperature and food availability can also affect the spatial distribution of the biomass of European hake through indirect effects. The general decrease in biomass of several stocks of anchovy and sardine observed at a Mediterranean scale (<xref ref-type="bibr" rid="CIT47">Vasilakopoulos et al. 2014</xref>) might be connected to the decreasing <italic>M. merluccius </italic>biomass detected in the present study along the time series. The effects of climate change (e.g. increase in mean SST, sea surface salinity and water stratification) are favouring smaller size classes in the zooplankton community (<xref ref-type="bibr" rid="CIT24">Herrmann et al. 2013</xref>). Since smaller plankton has lower energy values (<xref ref-type="bibr" rid="CIT50">Zarubin et al. 2014</xref>, <xref ref-type="bibr" rid="CIT06">Brosset et al. 2017</xref>), the condition of small pelagic fish may remain poor or even continue to decline, in turn having a negative effect on the European hake biomass, which would tend to decrease over time due to the reduced quality and/or quantity of food. Other investigations document that the decrease in biomass of small pelagic fish could arise from the combined effect of exploitation and environmental changes, especially in areas where anchovy and sardine were clearly overexploited, such as the Adriatic Sea (<xref ref-type="bibr" rid="CIT15">FAO 2014</xref>). Indeed, such decrease in the biomass of anchovy and sardine stocks could have negative effects on many Mediterranean fisheries, hindering productivity of both small pelagic stocks and species preying on small pelagic fish, such as European hake. </p>
			<p>The length analysis confirms, as other studies have reported (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT29">Ligas et al. 2015</xref>), that trawl fisheries in Mediterranean waters mainly target first age classes (0-1) corresponding to sizes of between 100 and 180 mm TL. The smaller median sizes of <italic>M. merluccius</italic> observed in the western Mediterranean (northern Spain and the Gulf of Lions) and in the central Mediterranean, mostly in the Ligurian and Cretan seas, probably confirm concentrations of juveniles in these areas (<xref ref-type="bibr" rid="CIT26">Hidalgo et al. 2009</xref>), whereas the largest median values, appearing in Corsican waters, the Aegean Sea and Cyprus waters, reflect the lack/low presence of recruits in these areas (<xref ref-type="bibr" rid="CIT33">Orsi Relini et al. 2002</xref>, <xref ref-type="bibr" rid="CIT11">Druon et al. 2015</xref>) due to the recruitment seasonal peak (autumn-winter), which escapes from MEDITS sampling.</p>
		  <p>The present study has shown good predictive results highlighting how environmental factors can affect the spatio-temporal distribution pattern of the European hake in the Mediterranean basin. The results emphasize the importance of integrating any existing knowledge about environmental drivers in the assessments of the ecosystems and fisheries, in line with the ecosystem approach to fisheries management and the requirements of the European Commission (<xref ref-type="bibr" rid="CIT13">EC 2008</xref>) to the Scientific, Technical and Economic Committee for Fisheries (STECF) and the International Council for the Exploration of the Sea (ICES). Furthering knowledge of the spatio-temporal distribution pattern of European hake and identifying persistent areas of high abundance of juveniles and spawners in relation to environmental factors are key elements for the management of this important commercial species that has long been overfished and is now subject to multi-annual management plans in several GSAs of the Mediterranean Sea (<xref ref-type="bibr" rid="CIT16">FAO 2016</xref>).</p>
		</sec>
		</body>
		<back>
<ack>
<title>ACKNOWLEDGEMENTS</title>
			<p>The MEDITS scientific survey is supported by the European Union Data Collection Framework and the Member States of the EU.</p>
		</ack>
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