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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-7745</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmars.2020.566686</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Marine Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>The Seasonal Distribution of a Highly Commercial Fish Is Related to Ontogenetic Changes in Its Feeding Strategy</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Lloret-Lloret</surname> <given-names>Elena</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/838401/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Navarro</surname> <given-names>Joan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/248814/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Gim&#x00E9;nez</surname> <given-names>Joan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/552869/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>L&#x00F3;pez</surname> <given-names>Nieves</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Albo-Puigserver</surname> <given-names>Marta</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/836616/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Pennino</surname> <given-names>Maria Grazia</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/582706/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Coll</surname> <given-names>Marta</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/174825/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Institut de Ci&#x00E8;ncies del Mar, Consejo Superior de Investigaciones Cient&#x00ED;ficas (ICM-CSIC)</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff>
<aff id="aff3"><sup>3</sup><institution>Marine Ecology Group, MaREI, Centre for Marine and Renewable Energy, Beaufort Building, Environmental Research Institute, University College Cork</institution>, <addr-line>Cork</addr-line>, <country>Ireland</country></aff>
<aff id="aff4"><sup>4</sup><institution>Instituto Espa&#x00F1;ol de Oceanograf&#x00ED;a, Centro Oceanogr&#x00E1;fico de Vigo</institution>, <addr-line>Vigo</addr-line>, <country>Spain</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Steven W. Purcell, Southern Cross University, Australia</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Davide Agnetta, National Institute of Oceanography and Experimental Geophysics (OGS), Italy; Germana Garofalo, Institute for Biological Resources and Marine Biotechnology (CNR), Italy</p></fn>
<corresp id="c001">&#x002A;Correspondence: Elena Lloret-Lloret, <email>elloret@icm.csic.es</email>; <email>elloret@cmima.csic.es</email></corresp>
<fn fn-type="other" id="fn004"><p>This article was submitted to Marine Fisheries, Aquaculture and Living Resources, a section of the journal Frontiers in Marine Science</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>18</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="collection">
<year>2020</year>
</pub-date>
<volume>7</volume>
<elocation-id>566686</elocation-id>
<history>
<date date-type="received">
<day>28</day>
<month>05</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>16</day>
<month>11</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2020 Lloret-Lloret, Navarro, Gim&#x00E9;nez, L&#x00F3;pez, Albo-Puigserver, Pennino and Coll.</copyright-statement>
<copyright-year>2020</copyright-year>
<copyright-holder>Lloret-Lloret, Navarro, Gim&#x00E9;nez, L&#x00F3;pez, Albo-Puigserver, Pennino and Coll</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract>
<p>Improving the knowledge on the biology, ecology and distribution of marine resources exploited by fisheries is necessary to achieve population recovery and sustainable fisheries management. European hake (<italic>Merluccius merluccius</italic>) is one of the most important target species in the Mediterranean Sea and is largely overexploited by industrial fisheries. Here, we used two methodological approaches to further investigate the seasonal variation in the spatial distribution of European hake considering ontogenetic changes and trophic ecology in the western Mediterranean Sea. Our main aim was to explore if spatial changes in hake distribution were related to trophic behavior, in addition to key environmental factors. We employed a hierarchical Bayesian species distribution modeling approach (B-SDM), using spatial data from two oceanographic surveys conducted during winter and summer. We analyzed how the environmental variables, together with abundance and mean weight distribution of the main preys identified for European hake, affected the seasonal distribution of the species. Results revealed clear differences in the distribution of the European hake between seasons, which were indeed partially correlated to the distribution of their main preys, in addition to the environment. Stable isotope values and Bayesian isotopic mixing models (MixSIAR) revealed substantial seasonal and ontogenetic differences in trophic habits of European hake, partly matching the spatial distribution results. These findings could have implications for a future seasonal-based adaptive fisheries management, as local depletion of prey, or variation in size and condition may affect European hake presence in this area. Moreover, this study illustrates how the sequential application of methodologies provides a more holistic understanding of species seasonality, which is essential to understand the phenological processes of exploited species and their potential shifts due to environmental changes.</p>
</abstract>
<kwd-group>
<kwd>European hake</kwd>
<kwd>fisheries</kwd>
<kwd>marine predator</kwd>
<kwd>mediterranean sea</kwd>
<kwd><italic>Merluccius merluccius</italic></kwd>
<kwd>spatial distribution</kwd>
<kwd>stable isotopes</kwd>
<kwd>trophic ecology</kwd>
</kwd-group>
<counts>
<fig-count count="6"/>
<table-count count="6"/>
<equation-count count="0"/>
<ref-count count="107"/>
<page-count count="17"/>
<word-count count="0"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1">
<title>Introduction</title>
<p>Identifying and understanding the main factors that affect the spatial distribution of marine organisms is important to evaluate the current distribution patterns and predict potential impacts of human activity (<xref ref-type="bibr" rid="B56">Lasram et al., 2010</xref>; <xref ref-type="bibr" rid="B70">Morfin et al., 2012</xref>). Changes in species distributions may be driven by environmental seasonal variation, as well as by prey availability (<xref ref-type="bibr" rid="B13">Carney, 2005</xref>; <xref ref-type="bibr" rid="B70">Morfin et al., 2012</xref>). Seasonal variations of life cycle events in animals and plants characterize the seasonal phenology and long term dynamics of a species, which is one of the most sensitive indicators to environmental changes (<xref ref-type="bibr" rid="B21">Cormon et al., 2014</xref>; <xref ref-type="bibr" rid="B92">Scranton and Amarasekare, 2017</xref>).</p>
<p>Seasonality takes place in all marine ecosystems, but their duration and intensity varies according to the geographical area, in general terms being more evident in tropical waters than in temperate waters (<xref ref-type="bibr" rid="B101">Valiela, 1995</xref>). Nevertheless, at a more regional scale, the western Mediterranean Sea is characterized by having a high seasonality (<xref ref-type="bibr" rid="B20">Coll et al., 2010</xref>); with a marked thermocline in summer and a lack of nutrients on the surface layer <italic>versus</italic> a mixing of the water column and an upraise of nutrients to the photic layer during winter (<xref ref-type="bibr" rid="B63">Margalef, 1985</xref>). Variations in environmental factors drive the distribution of nutrients and primary production, in fact phytoplankton blooms in this area peak in winter-spring coinciding with the stabilization of the water column and again in autumn, when the waters start to mix again (<xref ref-type="bibr" rid="B30">Estrada, 1996</xref>; <xref ref-type="bibr" rid="B90">Salat et al., 2002</xref>). The seasonal changes in environmental and biological variables in the entire Mediterranean basin is a well-studied subject (<xref ref-type="bibr" rid="B82">Psarra et al., 2000</xref>; <xref ref-type="bibr" rid="B9">Bosc et al., 2004</xref>; <xref ref-type="bibr" rid="B107">Zveryaev, 2015</xref>) and some studies have also been done on the effect of seasonality at different levels of biological organization (<xref ref-type="bibr" rid="B40">Gaertner, 2000</xref>; <xref ref-type="bibr" rid="B24">De Souza et al., 2011</xref>; <xref ref-type="bibr" rid="B84">Puerta et al., 2016</xref>). Nevertheless, the analysis of the interplay of seasonality with higher level trophic organisms tends to be scarce and patchy. Environmental factors have shown to affect species distribution (<xref ref-type="bibr" rid="B55">Katsanevakis et al., 2009</xref>; <xref ref-type="bibr" rid="B80">Pennino et al., 2013</xref>; <xref ref-type="bibr" rid="B73">Navarro et al., 2015</xref>; <xref ref-type="bibr" rid="B83">Puerta et al., 2015</xref>) and these environmental factors also show strong intra-annual variation in the Mediterranean Sea (<xref ref-type="bibr" rid="B90">Salat et al., 2002</xref>), therefore it could be relevant to take seasonality into account when analyzing species spatial patterns in this basin.</p>
<p>Several studies have used species distribution models (SDM) (<xref ref-type="bibr" rid="B46">Guisan and Zimmermann, 2000</xref>) to investigate the spatial patterns of marine species and most agreed on biological, environmental and human related variables affecting species distributions in the Mediterranean Sea (<xref ref-type="bibr" rid="B55">Katsanevakis et al., 2009</xref>; <xref ref-type="bibr" rid="B73">Navarro et al., 2015</xref>). On this context, several Mediterranean commercial marine species, including the European hake (<italic>Merluccius merluccius</italic>), present seasonality on their spatial distribution (<xref ref-type="bibr" rid="B25">Demestre and S&#x00E1;nchez, 1998</xref>; <xref ref-type="bibr" rid="B77">Paradinas et al., 2015</xref>; <xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>; Lloret-Lloret et al., under review). European hake is one of the most important demersal target species for commercial fisheries in the Mediterranean basin (<xref ref-type="bibr" rid="B91">S&#x00E1;nchez et al., 2007</xref>), but it is reaching overexploitation levels in numerous areas (<xref ref-type="bibr" rid="B58">Lleonart and Maynou, 2003</xref>; <xref ref-type="bibr" rid="B35">Fernandes et al., 2017</xref>) to the point that it is currently listed as Vulnerable species by the International Union for the Conservation of the Nature (IUCN)(<xref ref-type="bibr" rid="B27">Di Natale et al., 2011</xref>). Therefore, achieving a better understanding of spatial patterns of European hake and how they relate to seasonal processes could be of use to inform ecosystem-based management actions that result on more sustainable fisheries.</p>
<p>Due to the ecological and economic importance of European hake, different published studies had been focused on its spatial distribution (e.g., <xref ref-type="bibr" rid="B26">Demestre et al., 2000</xref>; <xref ref-type="bibr" rid="B1">Abella et al., 2005</xref>; <xref ref-type="bibr" rid="B29">Druon et al., 2015</xref>; <xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>). These studies indicated that European hake occupies a wide bathymetric distribution range (from 20 to 1,000 meters), inhabiting the shelf and upper slope in the Mediterranean Sea (<xref ref-type="bibr" rid="B37">Fisher et al., 1987</xref>; <xref ref-type="bibr" rid="B26">Demestre et al., 2000</xref>; <xref ref-type="bibr" rid="B76">Orsi-Relini et al., 2002</xref>). Oceanographically, this species is mainly present in cooler bottom waters (from 9.65 to 19.23&#x00B0;C) (<xref ref-type="bibr" rid="B62">Maravelias et al., 2006</xref>). Seasonal variations in the density of European hake have been recorded in some areas of the Mediterranean Sea and the Atlantic Ocean with movements to deeper and cooler waters during summer (<xref ref-type="bibr" rid="B34">Fari&#x00F1;a et al., 1997</xref>; <xref ref-type="bibr" rid="B61">Maravelias and Papaconstantinou, 2006</xref>; <xref ref-type="bibr" rid="B62">Maravelias et al., 2006</xref>; Lloret-Lloret et al. under review).</p>
<p>SDMs generally focus on abiotic factors, however species spatial patterns are also related to the availability and preference of prey, hence the importance of considering information on feeding strategies when analyzing the spatial distribution of a species (<xref ref-type="bibr" rid="B72">Navarro et al., 2016</xref>). In the case of European hake, many studies have analyzed its feeding ecology (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B14">Carpentieri et al., 2005</xref>; <xref ref-type="bibr" rid="B15">Cartes et al., 2009</xref>) but few have directly studied the relationship between trophic behavior and spatial distribution in the Mediterranean Sea (<xref ref-type="bibr" rid="B53">Johnson et al., 2012</xref>, <xref ref-type="bibr" rid="B52">2013</xref>).</p>
<p>European hake has been described as an ambush demersal predator (<xref ref-type="bibr" rid="B81">Pitcher and Alheit, 1995</xref>), which also feeds on the water column performing nocturnal diel migrations, important for the juvenile individuals (<xref ref-type="bibr" rid="B12">Bozzano et al., 2005</xref>; <xref ref-type="bibr" rid="B2">Aguzzi et al., 2015</xref>). Trophic studies of this species have generally focused on immature individuals (<xref ref-type="bibr" rid="B75">Oliver and Massuti, 1995</xref>; <xref ref-type="bibr" rid="B12">Bozzano et al., 2005</xref>) except for a few number that include a wider range of sizes (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B14">Carpentieri et al., 2005</xref>; <xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>). Despite these studies, there is still a lack of information on the ecology of European hake, especially regarding variation of the diet with seasonality.</p>
<p>The study of the diet of marine fish normally relies on stomach content analyses (<xref ref-type="bibr" rid="B50">Hyslop, 1980</xref>). Although this technique provides quantitative diet composition, it is limited by degradation and ingestion rates, amongst other constraints (<xref ref-type="bibr" rid="B50">Hyslop, 1980</xref>). In addition, high levels of regurgitation have been recorded for European hake (<xref ref-type="bibr" rid="B68">Modica et al., 2011</xref>), which difficult the analysis of the diet based on stomach content analysis. As an alternative, the use of stable isotopes analysis and isotopic mixing models are additional techniques to examine the diet of marine predators (<xref ref-type="bibr" rid="B23">Davis and Pineda-Munoz, 2016</xref>). Despite the extended use of these methodologies, only a small number of studies have used them to characterize the feeding ecology of European hake in the western Mediterranean Sea [e.g., (<xref ref-type="bibr" rid="B93">Sinopoli et al., 2012</xref>; <xref ref-type="bibr" rid="B32">Fanelli et al., 2018</xref>)] and more specifically in the northwestern Mediterranean Sea [e.g., (<xref ref-type="bibr" rid="B36">Ferraton et al., 2007</xref>; <xref ref-type="bibr" rid="B3">Albo-Puigserver et al., 2016</xref>; <xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>; <xref ref-type="bibr" rid="B89">Rueda et al., 2019</xref>)].</p>
<p>In this study, we aimed to investigate the seasonal differences in the spatial distribution and trophic habits of European hake in a highly exploited area of the northwestern Mediterranean Sea, considering ontogenetic variations. To analyze the seasonal spatial distribution, we employed a hierarchical Bayesian species distribution modeling approach (B-SDM) and we used the mean weight values estimated from two oceanographic surveys conveyed in winter and summer 2013, as a proxy of body size (<xref ref-type="bibr" rid="B18">Chih-Lin et al., 2010</xref>) and consequently age class. We expected that juvenile (i.e., lower mean weight) and adult individuals (i.e., higher mean weight) will distribute differently within the same season and between seasons and these differences might be explained by feeding preferences on top of environmental variables. We included depth and sea bottom temperature (SBT) as environmental variables, in addition to potential preys&#x2019; abundance and mean weight distribution as trophic components. Furthermore, we used stable isotope values of &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N to determine the ontogenetic and seasonal changes in diet using Bayesian mass-balanced isotopic mixing models (MixSIAR) (<xref ref-type="bibr" rid="B100">Stock and Semmens, 2016</xref>) and corrected standard ellipses area (SEA<sub>c</sub>) and Bayesian standard ellipses area (SEA<sub>b)</sub> using SIBER &#x2013; Stable Isotope Bayesian Ellipses in R (<xref ref-type="bibr" rid="B51">Jackson et al., 2011</xref>) to estimate niche width and overlap. Then, we examined if spatial differences did coincide with seasonal differences in the trophic habits and plasticity. This is to our knowledge one of the first studies to use this multidisciplinary approach to analyze the spatial and seasonal variations of European hake distribution and trophic ecology, accounting for ontogenetic changes. Specifically, we hypothesized that seasonal changes observed in European hake distribution could be partly due to prey availability, in addition to environmental variability, which could change between the juvenile and the adult stages of the population due to dissimilar trophic preferences or feeding capabilities.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="S2.SS1">
<title>Data Collection</title>
<p>In order to estimate biomass and abundance, European hake individuals were collected from two experimental fishing surveys conducted in winter (22 February-8 March) and summer (2&#x2013;17 July) 2013 (ECOTRANS Project, Institut de Ci&#x00E8;ncies del Mar - CSIC) on board of the RV &#x00C1;ngeles Alvari&#x00F1;o. These surveys were conducted in the northwestern Mediterranean Sea (<xref ref-type="fig" rid="F1">Figure 1</xref>) covering an extension of &#x223C;8,000 km<sup>2</sup>, including the continental shelf and upper slope. Sampling sites were randomly distributed over the continental shelf areas and the upper slopes, with a total of 82 hauls conducted; 37 in winter and 45 in summer (<xref ref-type="fig" rid="F1">Figure 1</xref>). Experimental fishing surveys were performed following EU-funded Mediterranean Trawl Survey (MEDITS) trawling protocols (see <xref ref-type="bibr" rid="B7">Bertrand et al., 2002</xref>) using a GO73 experimental mesh of 10 mm (stretched mesh). On board, all organisms were identified and classified to the lowest taxonomic level. In addition, the length frequency distribution (TL, in cm) and total number of individuals per haul was recorded. Biological sampling on board was done for 103 European hake&#x2019;s individuals, the body length (TL, in cm) and weight (to the nearest 0.001 g) was measured and a piece of muscle tissue was taken and frozen at &#x2212;20&#x00B0;C. Sampled individuals ranged from 7.3 to 50.2 cm in total length. We classified individuals into two size ranges (juveniles, TL &#x003C; 25 cm, and mature adults, TL &#x2265; 25 cm; <xref ref-type="supplementary-material" rid="TS1">Supplementary Table 1</xref>) based on the first maturity for this area of the Mediterranean Sea (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B57">Lleonart, 2002</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption><p>Study area and geographical positions of the sample sites of ECOTRANS survey for winter and summer.</p></caption>
<graphic xlink:href="fmars-07-566686-g001.tif"/>
</fig>
<p>Estimates of biomass (kg/km<sup>2</sup>) and abundance (n/km<sup>2</sup>) were calculated with the standard swept area method for all the species collected.</p>
</sec>
<sec id="S2.SS2">
<title>Bayesian Species Distribution Models</title>
<sec id="S2.SS2.SSS1">
<title>Explanatory and Response Variables</title>
<p>The biomass and abundance data for European hake was used to calculate mean weight data (kg/n) (biomass/abundance in kg/n) (<xref ref-type="bibr" rid="B18">Chih-Lin et al., 2010</xref>; <xref ref-type="bibr" rid="B42">Garofalo et al., 2018</xref>) per haul and was used as the response variable to develop the B-SDMs. As European hake weight increases with length and age, generally heavier individuals are larger and older (i.e., adults), whereas lighter individuals are smaller and younger (i.e., juveniles) (<xref ref-type="bibr" rid="B87">Recasens et al., 1998</xref>; <xref ref-type="bibr" rid="B66">Mellon-Duval et al., 2010</xref>; <xref ref-type="bibr" rid="B96">Soykan et al., 2015</xref>). According to the length-weight relationship for European hake for the western Mediterranean (a = 0.048, b = 3.055) (<xref ref-type="bibr" rid="B69">Morey et al., 2003</xref>), for an individual of 25 cm (adult), the corresponding mean weight would be around 0.0895 kg. Mean weight is used here as a proxy of body size and consequently age class (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 6</xref>).</p>
<p>Sea Bottom Temperature (SBT, in &#x00B0;C) and bathymetry (in meters, m) were selected as the main environmental explanatory predictors for the species distribution models. We chose these two environmental predictors based on previous studies conducted with European hake in the Mediterranean Sea (<xref ref-type="bibr" rid="B57">Lleonart, 2002</xref>; <xref ref-type="bibr" rid="B62">Maravelias et al., 2006</xref>; <xref ref-type="bibr" rid="B55">Katsanevakis et al., 2009</xref>; <xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>). Bathymetry data was obtained from EMODnet bathymetry (<ext-link ext-link-type="uri" xlink:href="http://portal.emodnet-bathymetry.eu/">http://portal.emodnet-bathymetry.eu/</ext-link>) and Sea Bottom Temperature (SBT, &#x00B0;C) for each haul was collected through CTDs conducted during the survey cruises (<xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 2</xref>). QGIS software (<xref ref-type="bibr" rid="B85">QGIS-Development-Team, 2012</xref>) was used to generate raster maps [Inverse Distance Weighted (IDW) interpolation], at a 0.1&#x00B0; x 0.1&#x00B0;; resolution) with the interpolate SBT data collected <italic>in situ</italic> during the survey to the entire study area for both seasons (<xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 2</xref>).</p>
<p>To create prey distributions variables, we first identified potential prey for European hake in the western Mediterranean Sea based on published studies (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>, <xref ref-type="bibr" rid="B12">2005</xref>; <xref ref-type="bibr" rid="B16">Cartes et al., 2004</xref>, <xref ref-type="bibr" rid="B15">2009</xref>; <xref ref-type="bibr" rid="B36">Ferraton et al., 2007</xref>; <xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>) (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 2</xref>). From the prey species identified in these studies, we selected those that were also collected during our surveys. We ended up with a total of 54 potential preys&#x2019; species in winter and 38 in summer (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 3</xref>), aggregated in two functional categories (fish and crustaceans), for each season. For both potential prey groups, total values of biomass (kg/km<sup>2</sup>) and abundance (n/km<sup>2</sup>), as well as mean weight (biomass/abundance, in kg/n), were calculated per haul. Abundance and mean weight data for each group were plotted and interpolated [Inverse Distance Weighted (IDW) interpolation] at a 0.1 &#x00D7; 0.1 degree spatial resolution, for both seasons, using QGIS software (<xref ref-type="bibr" rid="B85">QGIS-Development-Team, 2012</xref>; <xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption><p>Graphical interpretation of the results for the species distribution models (B-SDM) in winter <bold>(A)</bold> and summer <bold>(B)</bold>. The y-axis of the graphs show the response variable (European hake mean weight) and the x-axis the intensity (from lower to higher) of the explanatory variables of &#x201C;Fish&#x201D; and &#x201C;Crustaceans.&#x201D; The yellow lines represent &#x201C;Fish&#x201D; and the blue lines &#x201C;Crustaceans&#x201D;. In both cases, the straight line is used to represent the response variables related to abundance and the dotted line the response variables related to mean weight. Images from PhyloPic: European hake (&#x201C;<ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/8d92b454-3131-4bbd-ac9c-e1df14c2fc5a/">http://phylopic.org/image/8d92b454-3131-4bbd-ac9c-</ext-link> <ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/8d92b454-3131-4bbd-ac9c-e1df14c2fc5a/">e1df14c2fc5a/</ext-link>&#x201D;) this image available for reuse under the Public Domain Mark 1.0, Engraulidae (&#x201C;<ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/6bd3702d-3ef1-44d0-83bf-93377875017c/">http://phylopic.org/image/6bd3702d-3ef1-44d0-83bf-93377</ext-link> <ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/6bd3702d-3ef1-44d0-83bf-93377875017c/">875017c/</ext-link>&#x201D;) by M. Kolmann this image available for reuse under the Public Domain Mark 1.0 and <italic>Liocarcinus depurator</italic> &#x201C;<ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/01dd976b-f6e9-4204-bae1-c15a32234f73/">http://phylopic.org/image/01dd976b-</ext-link> <ext-link ext-link-type="uri" xlink:href="http://phylopic.org/image/01dd976b-f6e9-4204-bae1-c15a32234f73/">f6e9-4204-bae1-c15a32234f73/</ext-link>&#x201D;) by Hans Hillewaert (vectorized by T. Michael Keesey) this image available for reuse under the Creative commons attribution-share Alike 3.0 Unported. &#x201C;license.&#x201D;</p></caption>
<graphic xlink:href="fmars-07-566686-g002.tif"/>
</fig>
<p>Response variables were aggregated at the same spatial resolution of 0.1 &#x00D7; 0.1 degrees for each season using the &#x201C;raster&#x201D; package (<xref ref-type="bibr" rid="B48">Hijmans, 2018</xref>) in the R software (R version 3.5.1.) (<xref ref-type="bibr" rid="B86">R Core Team, 2018</xref>). Standardized data exploration techniques were used to identify any outliers and possible correlation and collinearity between the explicative variables (<xref ref-type="bibr" rid="B106">Zuur et al., 2010</xref>). In particular, all the variables were checked for linearity with the draftsman&#x2019;s plot, for multi-collinearity using the <italic>corvif</italic> function in R software (R version 3.5.1) (<xref ref-type="bibr" rid="B86">R Core Team, 2018</xref>) that assesses the Generalized Variance-Inflation Factors (GVIF) (<xref ref-type="bibr" rid="B38">Fox and Weisberg, 2011</xref>), and for correlation using Spearman measure with the <italic>corrplot</italic> function (<xref ref-type="bibr" rid="B104">Wei and Simko, 2017</xref>) in R software. A GVIF lower than 3 and a correlation lower than 0.70 were found for all the explanatory variables in winter and summer (<xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 8</xref>) (<xref ref-type="bibr" rid="B106">Zuur et al., 2010</xref>; <xref ref-type="bibr" rid="B28">Dormann et al., 2013</xref>). Moreover, to better interpret both the direction (positive or negative) and magnitudes (effect sizes) of the parameter estimates in relation to the others, the explanatory variables were standardized, <italic>i.e.</italic>, difference from the mean divided by the corresponding standard deviation (<xref ref-type="bibr" rid="B43">Gelman et al., 2008</xref>).</p>
</sec>
<sec id="S2.SS2.SSS2">
<title>Spatial Model Fitting, Estimation, Validation and Prediction</title>
<p>Hierarchical Bayesian species distribution models (B-SDMs) were implemented to identify the relationships of the explanatory variables with the European hake&#x2019;s mean weight and to map posterior predicted probabilities of this species in both seasons. In particular, we developed and compared four different models, two for each season, using the environmental characteristics corresponding to the season (bathymetry and SBT), and alternating the corresponding seasonal abundance and mean weight of the preys (fish and crustaceans, see <xref ref-type="table" rid="T1">Table 1</xref>).</p>
<table-wrap position="float" id="T1">
<label>TABLE 1</label>
<caption><p>Summary table of the four Bayesian species distribution models performed (<italic>Model 1, Model 2, Model 3, and Model 4</italic>).</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<tbody>
<tr>
<td valign="top" align="center">
<inline-graphic xlink:href="fmars-07-566686-t001.jpg"/>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>Spearman&#x2019;s spatial correlations (&#x03C1;), between observed and predicted values indicate the goodness of the predictions. The &#x002A; symbols marks the significance (<italic>p</italic>-value &#x003C; 0.01) of this correlation. The column &#x201C;Failures,&#x201D; indicates the sum of the failure vector calculated by the internal leave-one-out cross-validation of R-INLA. Gray cells indicate the included variables in each model. Variables acronyms are: Bat (Bathymetry, m), and SBT (Sea Bottom Temperature, &#x00B0;C). All models include the spatial effect.</italic></attrib>
</table-wrap-foot>
</table-wrap>
<p>Despite mean weight data could take any positive value, in this specific case the European hake&#x2019;s mean weight data ranged between 0 and 1. Therefore, for each model, we used a beta distribution Y<italic><sub><italic>i</italic></sub></italic> &#x223C; <italic>Be</italic> (&#x03BC;<italic><sub>i</sub></italic>, &#x03A6;<italic><sub>i</sub></italic>) for the response variable Y<italic><sub><italic>i</italic></sub></italic> at the location <italic>i</italic>. This type of beta distribution fulfils the required characteristics of this dataset while being very flexible in terms of shapes (<xref ref-type="bibr" rid="B47">Gupta and Nadarajah, 2004</xref>; <xref ref-type="bibr" rid="B78">Paradinas et al., 2016</xref>). Nevertheless, in the event that these kind of models are repeated and the mean weight values exceed 1, a beta distribution would not be suitable. In addition to the explanatory variables a spatial unstructured random effect was added to each model to account for the spatial correlation. For this spatial component, a multivariate Gaussian distribution with mean zero and a Mat&#x00E9;rn correlation matrix was assumed (<xref ref-type="bibr" rid="B71">Mu&#x00F1;oz et al., 2013</xref>). For the fixed effects, we assigned a non-informative zero-mean Gaussian prior distribution with a variance of 100, as no prior information was available. B-SDMs were performed using the Integrated Nested Laplace Approximation (INLA) methodology and software<sup><xref ref-type="fn" rid="footnote1">1</xref></sup> (<xref ref-type="bibr" rid="B88">Rue et al., 2009</xref>). To evaluate the goodness-of-fit of each model, we used the R-INLA (<xref ref-type="bibr" rid="B88">Rue et al., 2009</xref>) internal cross validation procedure, which consists on a leave-one-out cross-validations that generate a failure vector ranging from 0 to 1 (values equal to 0 mean that the predictive measure is reliable while values equal to 1 indicate that the predictive measure for that particular observation is not reliable) (<xref ref-type="bibr" rid="B8">Blangiardo and Cameletti, 2015</xref>).</p>
<p>Seasonal European hake&#x2019;s mean weight predictions were then generated predicting the response variable for the entire study area using linear interpolation via a Bayesian kriging (<xref ref-type="bibr" rid="B80">Pennino et al., 2013</xref>). To assess the fit of the predicted model, predicted and observed values were compared using using the <italic>corLocal</italic> function (<xref ref-type="bibr" rid="B48">Hijmans, 2018</xref>) of the R software that compute the Spearman&#x2019;s spatial correlations <italic>r</italic>. As usual, values of Spearman&#x2019;s correlation range from &#x2212;1 to 1, being 1 equal to a perfect positive correlation between the two datasets (<xref ref-type="bibr" rid="B97">Spearman, 1904</xref>).</p>
</sec>
</sec>
<sec id="S2.SS3">
<title>Stable Isotopes Analysis</title>
<p>All collected muscle hake samples were freeze-dried and powdered, and 0.28-0.33 mg of each sample was packed into tin capsules. Stable isotope analyses were performed at the Laboratorio de Is&#x00F3;topos Estables Estaci&#x00F3;n Biol&#x00F3;gica de Do&#x00F1;ana<sup><xref ref-type="fn" rid="footnote2">2</xref></sup>. Samples, were combusted at 1020&#x00B0;C using a continuous flow isotope-ratio mass spectrometry system by means of a Flash HT Plus elemental analyzer coupled to a Delta-V Advantage isotope ratio mass spectrometer via a CONFLO IV interface (Thermo Fisher Scientific, Bremen, Germany). The isotopic composition was reported in the conventional delta (&#x03B4;) per mil notation (&#x2030;), relative to atmospheric N<sub>2</sub> (&#x03B4;<sup>15</sup>N) and Vienna Pee Dee Belemnite (&#x03B4;<sup>13</sup>C). Replicate assays of standards routinely inserted within the sampling sequence indicated analytical measurement errors of &#x00B1; 0.2 and &#x00B1; 0.1 for &#x03B4;<sup>15</sup>N and &#x03B4;<sup>13</sup>C, respectively. The standards used were: EBD-23 (cow horn, internal standard), LIE-BB (whale baleen, internal standard) and LIE-PA (razorbill feathers, internal standard). These laboratory standards were previously calibrated with international standards supplied by the International Atomic Energy Agency (IAEA, Vienna). Because all samples showed a C:N ratio lower than 3.5&#x2030; we did not correct the &#x03B4;<sup>13</sup>C values to account for the presence of lipids in muscle samples (<xref ref-type="bibr" rid="B59">Logan and Lutcavage, 2008</xref>).</p>
<p>The values of &#x03B4;<sup>15</sup>N and &#x03B4;<sup>13</sup>C were used to calculate corrected standard ellipses area (SEA<sub><italic>c</italic></sub>, area containing 40% of the data) and Bayesian standard ellipses area (SEA<sub><italic>b</italic></sub>) as a measure of trophic width, using &#x201C;SIBER&#x201D; &#x2013; Stable Isotope Bayesian Ellipses in R (<xref ref-type="bibr" rid="B51">Jackson et al., 2011</xref>). These metrics allowed comparing the degree of niche width and overlap between seasons (winter vs. summer) and between life stages (adults <italic>vs</italic> juveniles) (<xref ref-type="table" rid="T3">Table 3</xref>). Differences on &#x03B4;<sup>15</sup>N and &#x03B4;<sup>13</sup>C values between hake ontogenetic groups (adults <italic>vs</italic> juveniles) and seasons (winter <italic>vs</italic> summer) were tested using a 2-way semiparametric permutation multivariate analyses (PERMANOVA tests) and pairwise tests on the Euclidian distance matrix with the software PRIMER-E 6 with PERMANOVA (<xref ref-type="bibr" rid="B4">Anderson et al., 2008</xref>); the latter only performed in the case of significance results (<italic>p</italic> &#x003C; 0.05) for the interaction of both factors (season<sup>&#x2217;</sup>stage).</p>
<p>To assess the relative contribution of different preys in the diet of juveniles and adults of European hake in winter and summer, we used the mass-balanced Bayesian stable isotope mixing model MixSIAR (<xref ref-type="bibr" rid="B99">Stock et al., 2018</xref>). Bayesian isotopic mixing models incorporate uncertainty in the consumers, sources and diet-to-tissue discrimination factors and are capable of producing robust estimates on complex dietary systems (<xref ref-type="bibr" rid="B79">Parnell et al., 2010</xref>), as it is the case of this generalist predator. Prey items included in the MixSIAR models were selected based on the diet published information from stomach content analysis (<xref ref-type="supplementary-material" rid="TS1">Supplementary Tables 2</xref>, <xref ref-type="supplementary-material" rid="TS1">4</xref>). Only those species/groups representing &#x003E; 5% of the stomach content in percentage of weight (%W) or index of relative importance (%IRI) were included in the MixSIAR models. Potential prey species were 21 (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 4</xref>). Stable isotope values from these identified prey species were taken from an isotopic database containing demersal and pelagic species collected during the same oceanographic survey as hake (ECOTRANS Project Isolibrary) and published literature (<xref ref-type="bibr" rid="B60">Madurell et al., 2008</xref>; <xref ref-type="bibr" rid="B31">Fanelli et al., 2009</xref>; <xref ref-type="bibr" rid="B102">Valls et al., 2014</xref>; <xref ref-type="bibr" rid="B5">Barr&#x00ED;a et al., 2015</xref>, <xref ref-type="bibr" rid="B6">2018</xref>; <xref ref-type="table" rid="T6">Table 6</xref>). In order to reduce the end-members of the mixing model <italic>a priori</italic> cluster analysis was performed. Potential preys were grouped in 5 different clusters based in their isotopic similarities after applied Ward&#x2019;s hierarchical cluster analysis. Some clusters are exclusively formed of crustaceans&#x2019; species; as it is the case of cluster 2, 3, and 4, whereas cluster 1 is exclusively formed of fish species. On the other hand, cluster 5 consists on a mixture of fish and crustaceans&#x2019; species. (Cluster 1: <italic>Cepola macrophthalma, Boops boops, Spicara maena, Spicara smaris, Argentina sphyraena, Trisopterus minutus, Lepidopus caudatus, Sardina pilchardus, Maurolicus muelleri, Gadiculus argenteus</italic>, and <italic>Micromesistius poutassou;</italic> Cluster 2: <italic>Chlorotocus crassicornis</italic>, <italic>Nematoscelis megalops</italic>, and <italic>Phronima sedentaria</italic>; Cluster 3: <italic>Vibilia armata</italic>; in violet, Cluster 4: <italic>Anchialina agilis</italic> and <italic>Meganyctiphanes norvegica</italic> and Cluster 5: <italic>Engraulis encrasicolus, Plesionika heterocarpus, Sardinella aurita</italic>, and <italic>Solenocera membranacea</italic>, <xref ref-type="fig" rid="F5">Figure 5</xref> and <xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 3</xref>). These five prey groups were likely to explain the isotopic signature of all the consumers as they all fell within the 95% probabilities of the mixing region (see <xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 4</xref>), verifying that the fitted model could correctly calculate the source contribution for all the consumers (<xref ref-type="bibr" rid="B95">Smith et al., 2013</xref>).</p>
<p>Five MixSIAR models were constructed (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 5</xref>) using season and stage as categorical variables, as well as total length as continuous variable. The continuous variable was included as the linear regression between the isotopic values of &#x03B4;<sup>15</sup>N and &#x03B4;<sup>13</sup>C and individual&#x2019;s total length (TL, in cm) was found to be significant (<xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 5</xref>). Model selection was based on the deviance information criterion (DIC) and on the relative support for each model (Leave-one-out cross validation (LOO) and Akaike weights) using <italic>compare_models</italic> function of the &#x201C;MixSIAR&#x201D; packages in R software (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 5</xref>). When running the models, a diet-to-tissue discrimination factors (DTDF) of &#x0394;<sup>13</sup>C = &#x2212;0.25 &#x03B4;<sup>13</sup>C &#x2212; 3.48 and &#x0394;<sup>15</sup>N = &#x2212;0.28 &#x03B4;<sup>15</sup>N + 5.88 was used (<xref ref-type="bibr" rid="B17">Caut et al., 2009</xref>). Convergence was assessed using the Geweke-test and Gelman-Rubin diagnostics. MixSIAR models were run on the &#x201C;extreme&#x201D; setting (with 3 Markov chains Monte Carlo (MCMC), 3,000,000 iterations, a burn-in-phase of 1,500,000 and a thinning of 100). Residual and process error were included in the models, except for when total length is a covariate, in which cases, following recommendations (<xref ref-type="bibr" rid="B99">Stock et al., 2018</xref>), process error was not included.</p>
</sec>
</sec>
<sec id="S3">
<title>Results</title>
<sec id="S3.SS1">
<title>Spatial Explanatory Variables</title>
<p>According to the winter models (<italic>Model 1</italic> and <italic>Model 2</italic>) there was a positive relationship of fish and crustaceans&#x2019; abundance with European hake mean weight and negative relationship with fish and crustaceans&#x2019; mean weight (<xref ref-type="table" rid="T1">Tables 1</xref>, <xref ref-type="table" rid="T2">2</xref>, <xref ref-type="fig" rid="F2">Figure 2</xref>). This means that adults of European hake (areas with higher mean weight) were related to higher abundances of small-size (low mean weight) fish and crustaceans (<xref ref-type="fig" rid="F2">Figure 2</xref>). The opposite occurred for low values of the response variable (areas with lower mean weight, thus more presence of juveniles) that were related to lower abundances of large-size (high mean weight) fish and crustaceans (<xref ref-type="fig" rid="F2">Figure 2</xref>). On the other hand, in summer (<bold><italic>Model 3</italic></bold> and <bold><italic>Model 4</italic></bold>), high values of the response variables (high mean weight, more presence of adults) were related to higher abundances of small-size (low mean weight) crustaceans and with lower abundance of large-size (high mean weight) fish. Again, the opposite occurred for low values of the response variable (areas with more presence of juveniles) that were related to higher abundances of small-size (low mean weight) fish and lower abundances of large-size (high mean weight) crustaceans (<xref ref-type="fig" rid="F2">Figure 2</xref>). Related to the environmental variables, overall SBT had a negative relationship with the response variable, meaning that the larger mean weight individuals (adults) prefer lower SBT and thus, colder temperatures (<xref ref-type="table" rid="T2">Table 2</xref> and <xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 6</xref>). According to previous results, overall bathymetry has a positive relationship with the European hake&#x2019;s mean weight on the continental shelf, meaning that larger mean weight individuals (adults) tend to prefer deeper waters (<xref ref-type="table" rid="T2">Table 2</xref> and <xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 6</xref>).</p>
<table-wrap position="float" id="T2">
<label>TABLE 2</label>
<caption><p>Summary of the posterior distribution for the four models (<italic>Model 1, Model 2, Model 3, and Model 4</italic>).</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left">Models</td>
<td valign="top" align="center">Season</td>
<td valign="top" align="left">Predictor</td>
<td valign="top" align="center">Mean</td>
<td valign="top" align="center">SD</td>
<td valign="top" align="center">Q<sub>0</sub>.<sub>025</sub></td>
<td valign="top" align="center">Q<sub>0</sub>.<sub>50</sub></td>
<td valign="top" align="center">Q<sub>0</sub>.<sub>975</sub></td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold><italic>Model 1</italic></bold></td>
<td valign="top" align="center">Winter</td>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">&#x2013;3.309</td>
<td valign="top" align="center">1.15</td>
<td valign="top" align="center">&#x2013;5.59</td>
<td valign="top" align="center">&#x2013;3.31</td>
<td valign="top" align="center">&#x2013;1.05</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Bathymetry</td>
<td valign="top" align="center">&#x2013;0.939</td>
<td valign="top" align="center">1.30</td>
<td valign="top" align="center">&#x2013;3.54</td>
<td valign="top" align="center">&#x2013;0.92</td>
<td valign="top" align="center">1.58</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Sea Bottom Temperature</td>
<td valign="top" align="center">0.001</td>
<td valign="top" align="center">0.16</td>
<td valign="top" align="center">&#x2013;0.32</td>
<td valign="top" align="center">0.00</td>
<td valign="top" align="center">0.32</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Fish (abundance)</td>
<td valign="top" align="center">0.098</td>
<td valign="top" align="center">0.08</td>
<td valign="top" align="center">&#x2013;0.07</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">0.23</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Crustacean (abundance)</td>
<td valign="top" align="center">0.066</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">&#x2013;0.07</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">0.20</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Model 2</italic></bold></td>
<td valign="top" align="center">Winter</td>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">&#x2013;2.356</td>
<td valign="top" align="center">0.85</td>
<td valign="top" align="center">&#x2013;4.07</td>
<td valign="top" align="center">&#x2013;2.34</td>
<td valign="top" align="center">&#x2013;0.72</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Bathymetry</td>
<td valign="top" align="center">0.185</td>
<td valign="top" align="center">0.92</td>
<td valign="top" align="center">&#x2013;1.70</td>
<td valign="top" align="center">0.21</td>
<td valign="top" align="center">1.93</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Sea Bottom Temperature</td>
<td valign="top" align="center">&#x2013;0.088</td>
<td valign="top" align="center">0.16</td>
<td valign="top" align="center">&#x2013;0.41</td>
<td valign="top" align="center">&#x2013;0.09</td>
<td valign="top" align="center">0.23</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Fish (mean weight)</td>
<td valign="top" align="center">&#x2013;0.070</td>
<td valign="top" align="center">0.08</td>
<td valign="top" align="center">&#x2013;0.22</td>
<td valign="top" align="center">&#x2013;0.07</td>
<td valign="top" align="center">0.09</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Crustacean (mean weight)</td>
<td valign="top" align="center">&#x2013;0.070</td>
<td valign="top" align="center">0.09</td>
<td valign="top" align="center">&#x2013;0.27</td>
<td valign="top" align="center">&#x2013;0.07</td>
<td valign="top" align="center">0.10</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Model 3</italic></bold></td>
<td valign="top" align="center">Summer</td>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">&#x2013;1.192</td>
<td valign="top" align="center">0.51</td>
<td valign="top" align="center">&#x2013;2.24</td>
<td valign="top" align="center">&#x2013;1.18</td>
<td valign="top" align="center">&#x2013;0.23</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Bathymetry</td>
<td valign="top" align="center">2.357</td>
<td valign="top" align="center">0.64</td>
<td valign="top" align="center">1.06</td>
<td valign="top" align="center">2.37</td>
<td valign="top" align="center">3.57</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Sea Bottom Temperature</td>
<td valign="top" align="center">&#x2013;0.048</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">&#x2013;0.20</td>
<td valign="top" align="center">&#x2013;0.04</td>
<td valign="top" align="center">0.08</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Fish (abundance)</td>
<td valign="top" align="center">&#x2013;0.204</td>
<td valign="top" align="center">0.07</td>
<td valign="top" align="center">&#x2013;0.37</td>
<td valign="top" align="center">&#x2013;0.20</td>
<td valign="top" align="center">&#x2013;0.08</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Crustacean (abundance)</td>
<td valign="top" align="center">0.016</td>
<td valign="top" align="center">0.04</td>
<td valign="top" align="center">&#x2013;0.06</td>
<td valign="top" align="center">0.02</td>
<td valign="top" align="center">0.08</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Model 4</italic></bold></td>
<td valign="top" align="center">Summer</td>
<td valign="top" align="left">Intercept</td>
<td valign="top" align="center">&#x2013;2.522</td>
<td valign="top" align="center">0.56</td>
<td valign="top" align="center">&#x2013;3.66</td>
<td valign="top" align="center">&#x2013;2.51</td>
<td valign="top" align="center">&#x2013;1.46</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Bathymetry</td>
<td valign="top" align="center">0.622</td>
<td valign="top" align="center">0.68</td>
<td valign="top" align="center">&#x2013;0.74</td>
<td valign="top" align="center">0.64</td>
<td valign="top" align="center">1.91</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Sea Bottom Temperature</td>
<td valign="top" align="center">&#x2013;0.094</td>
<td valign="top" align="center">0.09</td>
<td valign="top" align="center">&#x2013;0.28</td>
<td valign="top" align="center">&#x2013;0.09</td>
<td valign="top" align="center">0.07</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Fish (mean weight)</td>
<td valign="top" align="center">0.019</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">&#x2013;0.08</td>
<td valign="top" align="center">0.02</td>
<td valign="top" align="center">0.11</td>
</tr>
<tr>
<td/>
<td/>
<td valign="top" align="left">Crustacean (mean weight)</td>
<td valign="top" align="center">&#x2013;0.007</td>
<td valign="top" align="center">0.06</td>
<td valign="top" align="center">&#x2013;0.12</td>
<td valign="top" align="center">&#x2013;0.01</td>
<td valign="top" align="center">0.11</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>It includes the mean, the standard deviation (SD), the median (Q<sub>0.50</sub>) and a 95% credible interval (Q<sub>0.025</sub> - Q<sub>0.975</sub>), which is a central interval containing 95% of the probability under the posterior distribution. All models include the spatial effect.</italic></attrib>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T3">
<label>TABLE 3</label>
<caption><p>Summary table of &#x03B4;<italic><sup>1</sup></italic><sup>3</sup>C and &#x03B4;<sup>15</sup>N stable isotopes values of European hake for each age and season.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td/>
<td valign="top" align="center" colspan="4">&#x03B4;<sup>13</sup>C (&#x2030;)<hr/></td>
<td valign="top" align="center" colspan="4">&#x03B4;<sup>15</sup>N (&#x2030;)<hr/></td>
</tr>
<tr>
<td valign="top" align="left">Group</td>
<td valign="top" align="center">n</td>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">SD</td>
<td valign="top" align="center">min</td>
<td valign="top" align="center">max</td>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">SD</td>
<td valign="top" align="center">min</td>
<td valign="top" align="center">max</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Adults-winter</bold></td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">&#x2212;18.49</td>
<td valign="top" align="center">0.30</td>
<td valign="top" align="center">&#x2212;18.98</td>
<td valign="top" align="center">&#x2212;17.94</td>
<td valign="top" align="center">10.23</td>
<td valign="top" align="center">0.75</td>
<td valign="top" align="center">9.11</td>
<td valign="top" align="center">11.81</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Adults-summer</bold></td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">&#x2212;19.00</td>
<td valign="top" align="center">0.41</td>
<td valign="top" align="center">&#x2212;19.55</td>
<td valign="top" align="center">&#x2212;18.17</td>
<td valign="top" align="center">9.55</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">7.77</td>
<td valign="top" align="center">11.19</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Juveniles-winter</bold></td>
<td valign="top" align="center">28</td>
<td valign="top" align="center">&#x2212;19.48</td>
<td valign="top" align="center">0.45</td>
<td valign="top" align="center">&#x2212;20.11</td>
<td valign="top" align="center">&#x2212;18.62</td>
<td valign="top" align="center">7.98</td>
<td valign="top" align="center">0.75</td>
<td valign="top" align="center">7.02</td>
<td valign="top" align="center">9.93</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Juveniles-summer</bold></td>
<td valign="top" align="center">35</td>
<td valign="top" align="center">&#x2212;19.71</td>
<td valign="top" align="center">0.32</td>
<td valign="top" align="center">&#x2212;20.29</td>
<td valign="top" align="center">&#x2212;19.03</td>
<td valign="top" align="center">8.36</td>
<td valign="top" align="center">0.52</td>
<td valign="top" align="center">7.44</td>
<td valign="top" align="center">9.79</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>n: number of samples, SD: standard deviation, min: minimum values and max: maximum values.</italic></attrib>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="S3.SS2">
<title>European Hake&#x2019;s Spatial Distributions</title>
<p>The &#x201C;failure vector&#x201D; showed extremely low values in all cases (&#x003C;0.1), proving a good goodness of fit of the models (<xref ref-type="table" rid="T1">Table 1</xref>). Furthermore, Spearman&#x2019;s correlation between the predicted and observed mean weight also showed significant correlations in three of the four models (<xref ref-type="table" rid="T1">Table 1</xref>). The predicted posterior mean weight distribution of European hake showed spatial differences between winter and summer (<xref ref-type="fig" rid="F3">Figure 3</xref>). In winter, higher mean weight areas were predicted on the southern of the Ebro Delta, close to the coast; whereas in summer, higher mean weight were predicted in all the northern part of the study area, showing respectively clustering areas southern to the Ebro Delta and northern of Tarragona (<xref ref-type="fig" rid="F1">Figures 1</xref>, <xref ref-type="fig" rid="F3">3</xref>). Both winter models (<bold><italic>Model 1</italic></bold> and <bold><italic>Model 2</italic></bold>) predict very similar posterior spatial distribution patterns and same occurred for both summer models (<bold><italic>Model 3</italic></bold> and <bold><italic>Model 4</italic></bold>). The only distinguishable difference for models using fish and crustacean&#x2019;s abundance (<bold><italic>Model 2</italic></bold> and <bold><italic>Model 4</italic></bold>) being that the predicted areas of high mean weight are subtly more confined and concentrated (<xref ref-type="fig" rid="F3">Figures 3B&#x2013;D</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption><p>Predicted distribution of the posterior mean of the relative mean weight (kg/n) for European hake in winter <bold>(A,B)</bold> and summer <bold>(C,D)</bold> for each one of the four models developed. Model 1 and Model 3 consider prey explanatory variables on abundance measure, whereas Model 2 and Model 4 include prey explanatory variables on mean weight measure. All results have been standardized and share a common scale ranging from 0 to 1.</p></caption>
<graphic xlink:href="fmars-07-566686-g003.tif"/>
</fig>
</sec>
<sec id="S3.SS3">
<title>Stable Isotopes Results</title>
<p>&#x03B4;<sup>13</sup>C values of European hake ranged from &#x2212;20.29 &#x2030; to &#x2212;17.94 &#x2030;, and from 7.44 &#x2030; to 11.81 &#x2030; in the case of &#x03B4;<sup>15</sup>N values (<xref ref-type="table" rid="T3">Table 3</xref>). Significant differences were found for &#x03B4;<sup>13</sup>C between life stages (pseudo-F = 119.82, <italic>p</italic> &#x003C; 0.001) and season (pseudo-F = 22.72, <italic>p</italic> &#x003C; 0.001) but not for the interaction between season and stage (pseudo-F = 3.01, <italic>p</italic> = 0.08). For &#x03B4;<sup>15</sup>N values, we only found differences between stages (pseudo-F = 129.21, <italic>p</italic>-value &#x003C; 0.001) and for the interaction between season and stage (pseudo-F = 12.32, <italic>p</italic>-value &#x003C; 0.001). Pairwise analysis showed differences between juveniles and adults for summer (<italic>p</italic>-value &#x003C; 0.001) and winter (<italic>p</italic>-value &#x003C; 0.001). Differences were also observed in the case of juveniles and adults between winter and summer (<italic>p</italic>-values &#x003C; 0.05) (<xref ref-type="table" rid="T4">Tables 4</xref>, <xref ref-type="table" rid="T5">5</xref> and <xref ref-type="fig" rid="F4">Figure 4</xref>). Regarding the isotopic niche, the group of juveniles-summer presented the smallest standard ellipses area (SEA<sub><italic>B</italic></sub>), followed by adults-winter, juveniles-winter and finally, adults-summer (<xref ref-type="fig" rid="F4">Figure 4</xref> and <xref ref-type="table" rid="T4">Table 4</xref>).</p>
<table-wrap position="float" id="T4">
<label>TABLE 4</label>
<caption><p>Bayesian Standard Ellipses Area (SEA<sub><italic>B</italic></sub>) values for each life stage (Adult-Juvenile) and each season (winter-summer).</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td valign="top" align="left">Adults-winter</td>
<td valign="top" align="left">Adults-summer</td>
<td valign="top" align="left">Juveniles-winter</td>
<td valign="top" align="left">Juveniles-summer</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Min</bold></td>
<td valign="top" align="left">0.3109</td>
<td valign="top" align="left">0.3905</td>
<td valign="top" align="left">0.3412</td>
<td valign="top" align="left">0.2205</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Median</bold></td>
<td valign="top" align="left">0.5769</td>
<td valign="top" align="left">0.9245</td>
<td valign="top" align="left">0.5801</td>
<td valign="top" align="left">0.3864</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Mean</bold></td>
<td valign="top" align="left">0.5927</td>
<td valign="top" align="left">0.9773</td>
<td valign="top" align="left">0.5942</td>
<td valign="top" align="left">0.3934</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Max</bold></td>
<td valign="top" align="left">1.3676</td>
<td valign="top" align="left">2.8402</td>
<td valign="top" align="left">1.2206</td>
<td valign="top" align="left">0.7895</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>Min: minimum values and Max: maximum values.</italic></attrib>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T5">
<label>TABLE 5</label>
<caption><p>Percentage of Bayesian Standard Ellipses Area (SEA<sub><italic>B</italic></sub>) overlap between European hake adults and juveniles for summer and winter.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td valign="top" align="center">Adults-winter</td>
<td valign="top" align="center">Adults-summer</td>
<td valign="top" align="center">Juveniles-winter</td>
<td valign="top" align="center">Juveniles-summer</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Adults-winter</bold></td>
<td valign="top" align="center">-</td>
<td valign="top" align="center">46.1 (35.63- 56.02)</td>
<td valign="top" align="center">22.51 (14.35-29.96)</td>
<td valign="top" align="center">12.57 (3.96-23.94)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Adults-summer</bold></td>
<td valign="top" align="center">76.67 (59.38-90.68)</td>
<td valign="top" align="center">-</td>
<td valign="top" align="center">10.25 (2.51-19.91)</td>
<td valign="top" align="center">12.96 (1.80-22.48)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Juveniles-winter</bold></td>
<td valign="top" align="center">21.53 (16.21-31.12)</td>
<td valign="top" align="center">7.26 (1.33-13.40)</td>
<td valign="top" align="center">-</td>
<td valign="top" align="center">14.48 (6.16-23.30)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Juveniles-summer</bold></td>
<td valign="top" align="center">8.15 (2.06-15.77)</td>
<td valign="top" align="center">8.26 (1.13-16.07)</td>
<td valign="top" align="center">8.57 (4.21-15.07)</td>
<td valign="top" align="center">-</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>The 25% and 75% credible intervals of the overlap are given between parentheses.</italic></attrib>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T6">
<label>TABLE 6</label>
<caption><p>Number of samples, mean, standard deviation (SD) of &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N values of the prey used in the Bayesian mixing models to estimate the diet of European hake.</p></caption>
<table cellspacing="5" cellpadding="5" frame="hsides" rules="groups">
<thead>
<tr>
<td valign="top" align="left"></td>
<td/>
<td valign="top" align="center" colspan="2">&#x03B4;<sup>13</sup>C<hr/></td>
<td valign="top" align="center" colspan="2">&#x03B4;<sup>15</sup>N<hr/></td>
</tr>
<tr>
<td valign="top" align="left">Species</td>
<td valign="top" align="center">n</td>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">SD</td>
<td valign="top" align="center">mean</td>
<td valign="top" align="center">SD</td>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Cluster 1: Fish</bold></td>
<td valign="top" align="center">49</td>
<td valign="top" align="center">&#x2212;19.56</td>
<td valign="top" align="center">0.45</td>
<td valign="top" align="center">8.82</td>
<td valign="top" align="center">0.66</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Argentina sphyraena</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.19</td>
<td valign="top" align="center">0.44</td>
<td valign="top" align="center">9.30</td>
<td valign="top" align="center">1.05</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Boops boops</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.73</td>
<td valign="top" align="center">0.39</td>
<td valign="top" align="center">9.29</td>
<td valign="top" align="center">0.60</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Cepola macrophthalma</italic></bold></td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">&#x2212;20.40</td>
<td valign="top" align="center">0.05</td>
<td valign="top" align="center">8.16</td>
<td valign="top" align="center">0.04</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Gadiculus argenteus</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.43</td>
<td valign="top" align="center">0.13</td>
<td valign="top" align="center">8.85</td>
<td valign="top" align="center">0.69</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Lepidopus caudatus</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.71</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">8.43</td>
<td valign="top" align="center">0.24</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Maurolicus muelleri</italic></bold></td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">&#x2212;19.39</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">9.02</td>
<td valign="top" align="center">0.02</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Micromesistius poutassou</italic></bold></td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">&#x2212;19.43</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">8.83</td>
<td valign="top" align="center">0.25</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Sardina pilchardus</italic></bold></td>
<td valign="top" align="center">19</td>
<td valign="top" align="center">&#x2212;19.66</td>
<td valign="top" align="center">0.51</td>
<td valign="top" align="center">8.43</td>
<td valign="top" align="center">0.48</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Spicara maena</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.72</td>
<td valign="top" align="center">0.17</td>
<td valign="top" align="center">9.51</td>
<td valign="top" align="center">0.67</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Spicara smaris</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.15</td>
<td valign="top" align="center">0.57</td>
<td valign="top" align="center">9.58</td>
<td valign="top" align="center">1.04</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Trisopterus minutus</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.14</td>
<td valign="top" align="center">0.12</td>
<td valign="top" align="center">9.30</td>
<td valign="top" align="center">0.13</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Cluster 2: Crustaceans</bold></td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">&#x2212;20.06</td>
<td valign="top" align="center">0.72</td>
<td valign="top" align="center">6.49</td>
<td valign="top" align="center">0.57</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Chlorotocus crassicornis</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.71</td>
<td valign="top" align="center">0.63</td>
<td valign="top" align="center">6.99</td>
<td valign="top" align="center">0.47</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Nematoscelis megalops</italic></bold></td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">&#x2212;20.33</td>
<td valign="top" align="center">0.61</td>
<td valign="top" align="center">6.57</td>
<td valign="top" align="center">0.25</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Phronima sedentaria</italic></bold></td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">&#x2212;19.99</td>
<td valign="top" align="center">0.88</td>
<td valign="top" align="center">6.12</td>
<td valign="top" align="center">0.66</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Cluster 3: Crustaceans</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Vibilia armata</italic></bold></td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">&#x2212;21.24</td>
<td valign="top" align="center">0.10</td>
<td valign="top" align="center">3.79</td>
<td valign="top" align="center">0.42</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Cluster 4: Crustaceans</bold></td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">&#x2212;19.88</td>
<td valign="top" align="center">1.21</td>
<td valign="top" align="center">4.90</td>
<td valign="top" align="center">0.43</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Anchialina agilis</italic></bold></td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">&#x2212;19.47</td>
<td valign="top" align="center">1.39</td>
<td valign="top" align="center">4.82</td>
<td valign="top" align="center">0.51</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Meganyctiphanes norvegica</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;20.55</td>
<td valign="top" align="center">0.40</td>
<td valign="top" align="center">5.01</td>
<td valign="top" align="center">0.32</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Cluster 5: Mixed (Fish &#x0026; Crustaceans)</bold></td>
<td valign="top" align="center">36</td>
<td valign="top" align="center">&#x2212;18.89</td>
<td valign="top" align="center">0.35</td>
<td valign="top" align="center">8.12</td>
<td valign="top" align="center">0.88</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Boops boops</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;19.73</td>
<td valign="top" align="center">0.39</td>
<td valign="top" align="center">9.29</td>
<td valign="top" align="center">0.60</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Engraulis encrasicolus</italic></bold></td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">&#x2212;18.87</td>
<td valign="top" align="center">0.25</td>
<td valign="top" align="center">7.62</td>
<td valign="top" align="center">0.73</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Plesionika heterocarpus</italic></bold></td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">&#x2212;18.57</td>
<td valign="top" align="center">0.14</td>
<td valign="top" align="center">8.09</td>
<td valign="top" align="center">0.38</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>Sardinella aurita</italic></bold></td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">&#x2212;18.77</td>
<td valign="top" align="center">0.18</td>
<td valign="top" align="center">8.78</td>
<td valign="top" align="center">0.55</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<attrib><italic>Summary statistics are provided for Cluster 1, 2, 3, 4, and 5. n: number of samples.</italic></attrib>
</table-wrap-foot>
</table-wrap>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption><p><bold>(A)</bold> &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N values and corrected Standard Ellipses Area (SEAc) for European hake by season and stage. <bold>(B)</bold> Bayesian standard ellipses area (SEA<sub><italic>B</italic></sub>) for each groups. Density plots represent the 95, 75 and 50% credibility intervals of SEA<sub><italic>B</italic></sub>.</p></caption>
<graphic xlink:href="fmars-07-566686-g004.tif"/>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption><p>Individual European hake &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N values and mean and standard deviation &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N values of the potential prey sources. Black shaded scale of European hake represents the body length of the individuals. In brown, <bold>Cluster 1</bold>: <italic>Cepola macrophthalma, Boops boops, Spicara maena, Spicara smaris, Argentina sphyraena, Trisopterus minutus, Lepidopus caudatus, Sardina pilchardus, Maurolicus muelleri, Gadiculus argenteus</italic> and <italic>Micromesistius poutassou;</italic> in green, <bold>Cluster 2</bold>: <italic>Chlorotocus crassicornis</italic>, <italic>Nematoscelis megalops</italic> and <italic>Phronima sedentaria</italic>; in blue, <bold>Cluster 3</bold>: <italic>Vibilia armata</italic>; in violet, <bold>Cluster 4</bold>: <italic>Anchialina agilis</italic> and <italic>Meganyctiphanes norvegica</italic> and in aquamarine, <bold>Cluster 5</bold>: <italic>Engraulis encrasicolus, Plesionika heterocarpus, Sardinella aurita</italic> and <italic>Solenocera membranacea.</italic></p></caption>
<graphic xlink:href="fmars-07-566686-g005.tif"/>
</fig>
<p>The best MixSIAR model was the one that included fish length as a continuous variable and season as a factor (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 5</xref>). Outputs indicated that the relative importance of the different prey groups in the diet of European hake changed with season and fish length (cm) (<xref ref-type="fig" rid="F6">Figure 6</xref>). In winter, the smallest European hakes consumed mainly the prey included in Cluster 3, followed by Cluster 4 and Cluster 5. As we moved to larger sizes, there was a sharp decrease in Cluster 3 and an increase in Cluster 2. Moreover, Cluster 4 also decreased with size, representing almost 0% of the diet proportion for the largest individuals. The opposite occurred for Cluster 2, which was absent in the diet of small individuals and its proportion increased with fish length. Noteworthy, the highest proportion of Cluster 5 occurred for intermediate fish lengths. On the other hand, in summer, Cluster 4 dominated the diet of the smallest individuals followed by Cluster 3, the latter at a lower proportion than in winter. The proportion of Cluster 4 decreased with fish length at the same time that Cluster 2 increased. During summer, Cluster 5 represented lower proportion for all fish length, but maintained maximum values at intermediate fish length. Cluster 1 appeared similar to winter results.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption><p>MixSIAR model results showing estimated diet proportions of each potential prey clusters (1, 2, 3, 4, and 5) contributing to European hake diet, as a function of length (in cm) for each season (<bold>(A):</bold> winter and <bold>(B):</bold> summer). <bold>Cluster 1</bold>: <italic>Cepola macrophthalma, Boops boops, Spicara maena, Spicara smaris, Argentina sphyraena, Trisopterus minutus, Lepidopus caudatus, Sardina pilchardus, Maurolicus muelleri, Gadiculus argenteus</italic>, and <italic>Micromesistius poutassou</italic>, <bold>Cluster 2</bold>: <italic>Chlorotocus crassicornis</italic>, <italic>Nematoscelis megalops</italic>, and <italic>Phronima sedentaria</italic>, <bold>Cluster 3</bold>: <italic>Vibilia armata</italic>, <bold>Cluster 4</bold>: <italic>Anchialina agilis</italic> and <italic>Meganyctiphanes norvegica</italic>, and <bold>Cluster 5</bold>: <italic>Engraulis encrasicolus, Plesionika heterocarpus, Sardinella aurita</italic>, and <italic>Solenocera membranacea.</italic></p></caption>
<graphic xlink:href="fmars-07-566686-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="S4">
<title>Discussion</title>
<p>The spatial distribution of marine species is known to be affected by interannual and seasonal environmental factors and habitat parameters, but also by species interactions factors (<xref ref-type="bibr" rid="B44">Gilinsky, 1984</xref>; <xref ref-type="bibr" rid="B49">Hixon and Carr, 1997</xref>; <xref ref-type="bibr" rid="B13">Carney, 2005</xref>; <xref ref-type="bibr" rid="B70">Morfin et al., 2012</xref>). In the present study, we applied a multidisciplinary approach that analyses the spatial distribution of European hake, using its main environmental and feeding related drivers explaining European hake distribution. For this, we applied Hierarchical Bayesian species distribution models (B-SDMs) and stable isotope analysis, considering a seasonal and ontogenetic perspective. Overall, our results showed seasonal and ontogenetic differences in the spatial distribution of European hake in the northwestern Mediterranean Sea. In addition to the effect of particular environmental factors, these spatial changes were partially explained by the distribution of the main prey consumed by European hake. We also gathered information about the ontogenetic and seasonal variation in European hake diet to provide further insight into how feeding preferences may affect species spatial occurrence.</p>
<p>Our species distribution models showed that adult individuals had a tendency to be present in deeper areas, with lower bottom temperatures. These environmental preferences have already been described for this species in the Mediterranean Sea (<xref ref-type="bibr" rid="B25">Demestre and S&#x00E1;nchez, 1998</xref>; <xref ref-type="bibr" rid="B62">Maravelias et al., 2006</xref>; <xref ref-type="bibr" rid="B55">Katsanevakis et al., 2009</xref>; <xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>). Particularly, bathymetry has repeatedly been reported as a main driver in hake spatial distribution (<xref ref-type="bibr" rid="B87">Recasens et al., 1998</xref>; <xref ref-type="bibr" rid="B76">Orsi-Relini et al., 2002</xref>; <xref ref-type="bibr" rid="B64">Maynou et al., 2003</xref>; <xref ref-type="bibr" rid="B77">Paradinas et al., 2015</xref>). The importance of temperature and depth as key factors on SDMs has also been reported to vary intra-annually. For example, in the Aegean Sea (Central Mediterranean Sea) in 2016, the effect of temperature in the distribution of European hake was more pronounced during months of thermal stratification (<xref ref-type="bibr" rid="B105">Yal&#x00E7;ln and Gurbet, 2016</xref>). Indeed the influence of environmental factors on determining abundance of demersal fish in general are reported to be intensified during summer and autumn in the Aegean Sea (<xref ref-type="bibr" rid="B55">Katsanevakis et al., 2009</xref>). Furthermore, a seasonal and ontogenetic migration along the bathymetric gradient was also observed for European hake outside the Mediterranean Sea, for example in the Galician waters (Atlantic Ocean) (<xref ref-type="bibr" rid="B34">Fari&#x00F1;a et al., 1997</xref>).</p>
<p>Our results showed a spatial segregation between adult and juvenile European hakes, with adults mainly present in the southern area during winter and in the northern area during summer. Ontogenetic spatial differentiation and areas of aggregation for this species have previously been related to life cycle events (<xref ref-type="bibr" rid="B25">Demestre and S&#x00E1;nchez, 1998</xref>), nursery areas (<xref ref-type="bibr" rid="B29">Druon et al., 2015</xref>), areas with high food availability (<xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>) or changes in diet (<xref ref-type="bibr" rid="B14">Carpentieri et al., 2005</xref>; <xref ref-type="bibr" rid="B42">Garofalo et al., 2018</xref>). In general, it seems that adult females choose deeper areas whereas juveniles prefer the shallower continental shelf (<xref ref-type="bibr" rid="B25">Demestre and S&#x00E1;nchez, 1998</xref>). For instance, a study from late spring of 2015 using the same modeling approach than us detected three main hotspots area for European hake nurseries in the northwestern Mediterranean Sea, one of the three located northern of the Ebro Delta, similar to the distribution found here for adults&#x2019; hake in winter (<xref ref-type="bibr" rid="B77">Paradinas et al., 2015</xref>). In this context, European hake in the western Mediterranean Sea presents a spawning peak during autumn and winter (<xref ref-type="bibr" rid="B64">Maynou et al., 2003</xref>), hence the aggregation found close to the Ebro Delta during winter in our study might also correspond to a reproduction/nursery area. Furthermore, changes in the spatial distribution between seasons have also previously been detected in our study area, with individuals intensifying their spatial differentiation during summer and spring (<xref ref-type="bibr" rid="B25">Demestre and S&#x00E1;nchez, 1998</xref>).</p>
<p>In addition to the importance of the environmental variables on the distribution of European hake and although bathymetry showed the highest impact in all four models, the distribution of European hake main prey, both fish and crustaceans, also provided valuable information explaining the distribution of hake in the northwestern Mediterranean Sea. Specifically, the spatial distributions and abundance of potential preys were used as indirect proxies of trophic behavior in species spatial occurrence (<xref ref-type="bibr" rid="B72">Navarro et al., 2016</xref>). However, just including prey distribution was not enough in this case, as European hake is an ambush predator and its mouth gape is a limiting factor for prey selection and ingestion (this being especially true for juvenile individuals) (<xref ref-type="bibr" rid="B53">Johnson et al., 2012</xref>). This is why we also considered the information about potential preys&#x2019; size as an explanatory variable. In this regard, our results indicated that European hake&#x2019;s juveniles in winter were associated with low abundance of relatively large fish preys. The size of these individuals could potentially outbound the mouth gape limitations of the predators, and thus juvenile&#x2019;s European hake would mostly be feeding on small crustaceans as has been described in previous feeding studies (<xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>). However, in summer, juveniles are correlated with smaller and more abundant fish prey implying a potential capacity of also ingesting available small fish, in addition to crustaceans. Nevertheless, these results on their own were not sufficient to drawn a robust conclusion and the use of stable isotope analyses complemented the SDMs results providing additional information of European hake seasonal and ontogenetic trophic metrics.</p>
<p>In this context, differences in &#x03B4;<sup>13</sup>C and &#x03B4;<sup>15</sup>N values pointed out to potential differences in trophic habits of adults and juveniles between winter and summer. Furthermore, the low overlap between the different isotopic niches showed a strong degree of niche differentiation between seasons and stages, also confirmed by the mixing model. More specifically, the wide isotopic niche observed for adults suggests a more diversified feeding behavior, contrasting with the narrow isotopic niche breadth recorded for juveniles. This is in contrast with previous studies, in the same area and in the Tyrrhenian Sea (central Mediterranean Sea), that have described juveniles as being more opportunistic than adults (<xref ref-type="bibr" rid="B67">Modica et al., 2013</xref>). Our isotopic results also suggest a seasonal differentiation in adults&#x2019; diet, with higher consumption of crustaceans during summer compared to winter. To a certain extent, this relates to our observations of the SDMs, as adults&#x2019; hake during summer were correlated with small-size crustaceans. Related to juveniles, the differences in &#x03B4;<sup>15</sup>N values when compared to adults, and the positive trend with body size (<xref ref-type="supplementary-material" rid="FS1">Supplementary Figure 5</xref>) indicated ontogenetic variations in diet; with the lower values recorded for juveniles suggesting a consumption of lower trophic level organisms. In addition, MixSIAR outputs showed that cluster 3 (formed uniquely of the amphipod <italic>Vibilia armata</italic>) represented the highest diet proportion for juveniles in winter, followed by cluster 4 (formed exclusively of crustaceans) and the same clusters but in the opposite level of importance dominated for summer. This concurred with previous findings that reported a diet based on crustaceans for juveniles of European hake (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B16">Cartes et al., 2004</xref>, <xref ref-type="bibr" rid="B15">2009</xref>; <xref ref-type="bibr" rid="B36">Ferraton et al., 2007</xref>).</p>
<p>However, partly contrasting the results of the B-SDM, juveniles larger than 20 cm in winter showed a higher proportion of cluster 5 (formed by several crustaceans and small fish, <italic>i.e</italic>., <italic>Sardinella aurita</italic> and <italic>Engraulis encrasicolus</italic>) when compared to juveniles from summer. Consumption of small pelagic fish has also been recorded for this species for individuals as small as 7 cm in nearby areas (Gulf of Lions) (<xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>). Within this context, the SIA results suggested that summer juveniles may have been feeding on higher trophic levels prey sources, as they showed higher &#x03B4;<sup>15</sup>N values (<xref ref-type="bibr" rid="B74">Navarro et al., 2011</xref>) compared to juveniles in winter. However, rather than an increase of piscivorous diet during summer, it could be due to the fact that at this time of the year, juveniles feed more on cluster 4 than cluster 3, which occupied a slightly higher isotopic position. Nevertheless, it is important to consider that changes in &#x03B4;<sup>15</sup>N values can also reflect variations in the baseline rather than in diet, especially when comparing values from different seasons (<xref ref-type="bibr" rid="B22">Costalago et al., 2012</xref>; <xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>). Thus, further and more extensive analysis that include this aspect should be done to verify this seasonal variation in the diet.</p>
<p>Overall, our trophic analysis results were partly consistent with published data (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B16">Cartes et al., 2004</xref>, <xref ref-type="bibr" rid="B15">2009</xref>; <xref ref-type="bibr" rid="B36">Ferraton et al., 2007</xref>), where juveniles showed a crustaceans dominated diet and switch to a more piscivorous diet as they reached larger body size. Here we observed an evident ontogenetic variation in diet, with increased consumption of fish preys as individuals reach intermediate and large body size. We also observed that the species of crustaceans consumed varied with ontogenetic stage and season. However, not all the studies concur on the body length and the reason why this switch occurs. It has been suggested that the diet&#x2019;s switch corresponds to an increase in individual&#x2019;s mobility in the water column (<xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>) along a proportional increase in mouth dimensions (<xref ref-type="bibr" rid="B54">Karpouzi and Stergiou, 2003</xref>) that facilitates ingestion of larger preys (<xref ref-type="bibr" rid="B67">Modica et al., 2013</xref>). Piscivorous diet has also been associated with the retinal changes and the improvement in vision acuity (<xref ref-type="bibr" rid="B10">Bozzano and Catal&#x00E1;n, 2002</xref>) that permit European hake detects prey from higher distances and even in more turbid waters. These physiological changes altogether will ease prey selectivity and an intake preference for higher energy preys (<xref ref-type="bibr" rid="B98">Stagioni et al., 2011</xref>; <xref ref-type="bibr" rid="B67">Modica et al., 2013</xref>).</p>
<p>Overall, our results show that seasonality plays a role when looking at the distribution and feeding behavior of European hake. Seasonality can affect prey availability and thus predators&#x2019; diet. In our study, despite diet variations were more distinct between stages, differences between seasons should not be disregarded. Previous studies have also recorded diet variation with seasonality in the Western Mediterranean Sea (<xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>) and non-Mediterranean areas (<xref ref-type="bibr" rid="B103">Velasco and Olaso, 1998</xref>), with mixed conclusions. A study from the north-eastern Mediterranean Sea showed an increase in fish ingestion in winter and dominance of crustaceans during summer (<xref ref-type="bibr" rid="B98">Stagioni et al., 2011</xref>). This is partly in line with our results, as despite high proportions of cluster 2, 3, and 4 (formed exclusively of crustaceans) were observed during both seasons, consumption of cluster 5 (formed of fish and crustaceans) was overall higher during winter. Some studies have also recorded an increase on juveniles feeding on euphausiids in spring, coinciding with the aggregating reproductive behavior of this species (<xref ref-type="bibr" rid="B36">Ferraton et al., 2007</xref>), as well as an increase of gobiids consumption during autumn. Furthermore, a study from the Gulf of Lyon (northwestern Mediterranean Sea) found lower proportion of pelagic fish ingestion in adults&#x2019; diet in spring when compared to autumn (<xref ref-type="bibr" rid="B65">Mellon-Duval et al., 2017</xref>).</p>
<p>Despite valuable information, and the novelty and benefits of using a continuous covariate in the MixSIAR analyses, which is a relatively novel technique (<xref ref-type="bibr" rid="B39">Francis et al., 2011</xref>; <xref ref-type="bibr" rid="B100">Stock and Semmens, 2016</xref>; <xref ref-type="bibr" rid="B41">Gagne et al., 2018</xref>; <xref ref-type="bibr" rid="B99">Stock et al., 2018</xref>; <xref ref-type="bibr" rid="B45">Gorman et al., 2019</xref>), this study is subjected to some limitations. Unfortunately, this study only covers one year and it is not possible to prove repeatability of the seasonal pattern observed in the analysis. Moreover, the use of the mean weight as a proxy of European hake size and life stages in the B-SDM is appropriate to identify areas mostly occupied by juveniles and large adults in winter and summer. This type of variable can be of interest when wanting to account for changes related to life stages but where data of individuals&#x2019; body length is scarce, not available or not feasible. For example, oceanographic surveys where total biomass and abundance per haul is usually recorded but the individual sampling of all specimens for biological analysis is unrealistic. However, this kind of proxy also presents some limitations, not directly informing of the less extreme phases of the life cycle, as intermediate values of mean weight cannot be categorized into adults other juveniles and are likely composed of mixed life stages. Furthermore, according to the length-weight relationship for European hake in the western Mediterranean Sea (<xref ref-type="bibr" rid="B69">Morey et al., 2003</xref>), for an individual of 25 cm, which is the length above which we classified individuals as &#x201C;adults&#x201D; (<xref ref-type="bibr" rid="B11">Bozzano et al., 1997</xref>; <xref ref-type="bibr" rid="B57">Lleonart, 2002</xref>), the corresponding mean weight is around 0.0895 kg. This means that for our data, only a reduced number of hauls had mean weight values over this threshold (<xref ref-type="supplementary-material" rid="TS1">Supplementary Table 6</xref>). This translates into an under-representation of adult individuals, especially during summer. This is partly related to the fact that all the data for this study was collected through bottom trawling, whereas generally larger individuals of European hake are caught trough long net gears. This means that adults were not very abundant and that our maximum individuals&#x2019; size is 50.2 cm, but European hake in these waters can reach body lengths of more than 80 cm. Therefore, the diet and spatial distribution described here is not representing the entire population, omitting the largest individuals.</p>
<p>Our findings could have clear implications for a future seasonal-based adaptive fisheries management, as local depletion of prey, or variation in prey size and/or condition may affect hake presence in an area. This is of particular interest for this study as overfishing has led to a general decrease on demersal species (<xref ref-type="bibr" rid="B33">FAO-MED, 2018</xref>), some of which are prey to European hake, as well as a decline in small pelagic fish (e.g., <italic>Sardina pilchardus</italic> and <italic>Engraulis encrasicolus</italic>) (<xref ref-type="bibr" rid="B19">Coll and Bellido, 2019</xref>). Indeed, previous studies have already expressed their concerns about the underlying effect of these alterations for hake&#x2019;s population stock and distribution (<xref ref-type="bibr" rid="B94">Sion et al., 2019</xref>). Combining this information with future climate-change scenarios could result on major variations of the potential distribution of European hake in this area. Therefore, this study presents important information in the context of phenological processes of exploited species and their potential changes due to climate change. It does also emphasize, the need for additional monitoring efforts that consider a seasonal sampling of the marine ecosystems. Further information about spatial trophic analysis with season and ontogenetic stages is necessary if we want to fully understand species ecological roles, spatial-temporal population and food-web dynamics within marine ecosystems.</p>
</sec>
<sec id="S5">
<title>Data Availability Statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec id="S6">
<title>Ethics Statement</title>
<p>The authors declare that all fieldwork was approved by the Ethics Committee of CSIC, in accordance with the Spanish and EU legislation on the protection of animals used for scientific purposes.</p>
</sec>
<sec id="S7">
<title>Author Contributions</title>
<p>EL-L, MC, JN, and MA-P: conceptualization and design of the experiment. MC, JN, NL and MA-P: samples collection. NL and MA-P: samples preparations. EL-L, MA-P, and JG: formal analysis. EL-L: writing&#x2013;original draft preparation. MP, JN, MC, JG, MA-P, NL, and EL-L: writing&#x2013;reviewing and editing. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</body>
<back>
<fn-group>
<fn fn-type="financial-disclosure">
<p><bold>Funding.</bold> This study took advantage of data collected under the ECOTRANS projects (CTM2011-26333, 2012&#x2013;2014, Spanish Ministry of Economy and Competiveness, Spain). EL-L was supported by a FPU grant (FPU1704395, Spanish Ministry of Education). JN was supported by the Spanish National Program Ram&#x00F3;n y Cajal (RYC-2015-17809, Spanish Ministry of Economy and Competiveness, Spain). Also funded by Project IMPRESS (RTI2018-099868-B-I00) project, ERDF, Ministry of Science, Innovation and Universities &#x2013; State Research Agency.</p>
</fn>
</fn-group>
<ack>
<p>The authors would like to acknowledge all participants of the ECOTRANS project that helped in the sampling and laboratory work. We also would like to acknowledge Brian Stock for his assistance and support on developing the continuous MixSIAR models. This study is a contribution of the projects PELWEB (CTM2017-88939-R) and RESNEP (CTM2017-82991-C2-1-R) of the Spanish Ministry of Economy and Competiveness of Spain). The authors declare that all experimental procedures were conducted in strict accordance with good animal practice as defined by the current Spanish, Catalonian and European legislation.</p>
</ack>
<sec id="S10" sec-type="supplementary material"><title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmars.2020.566686/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmars.2020.566686/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.zip" id="FS1" mimetype="application/zip" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Data_Sheet_2.docx" id="TS1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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