Emmanuelle Dortel

Andrea Thiebault

Justin Amande

Jeanne Fortilus

Louis Dubuisson

Iker Lysel Garavelli

Ana Giannina Passuni

Rocio Alegre

Daniel Joo

Julian Uribe

Sonia Bernal

Andrea Reyes

Beenesh Motah

Su Familia

Iker Zudaire

Giannina Peruanos

Grados

Mis Agradecimientos

Julián Uribe

Carlos Varón

Diva Quintero

Zudaire for the good moments

Abstract

This study provides an approach to the comprehension of the mesoscale structures in the habitat of three major tropical tunas species, yellowfin (Thunnus albacares; YFT), skipjack (Katsuwonus pelamis; SKJ) and bigeye (Thunnus obesus: BET). These species, mainly caught by the purse seiner's fishery worldwide, represent 20% and 13% of the world total production for the Indian Ocean (IO) and the Eastern Pacific Ocean (EPO) respectively. Single set records from this fishing gear in both oceans, were used to separately evaluate the environmental characteristics of their fishing grounds for three fishing modes: free school (FS), log (LOG) and fish aggregating devices (FADs) and several fish sizes. Prior to the analyses, a statistical and expert-based method was applied to detect and classify thermal fronts at basin scale whereas distances of tuna catch positions to eddy structures (anticyclonic and cyclonic) were calculated based on the detection method of winding angle. We found that strong fronts are mostly found in coastal regions and weak fronts in the open ocean. The consideration of the frontal occurrence and intensity helps to spatially differentiate mechanisms of frontogenesis than may attract tunas. We also observed clear differences about distances, between eddies and tunas catch positions depending on fish-size.

In addition to these mesoscale components, other categories of variables as classic, temporal and fishery-related ones were added to describe the environment of the fishing grounds, based on tuna catch locations. We then used the Boosted Regression Tree (BRT) method to create a three step modeling scheme for each study area, first to explore the responses of the catch level and fish-size to the different fishing modes; then to consider the effect of simulated randomly distributed catch positions, in order to estimate the mesoscale effects; finally to explore the relative dominance of each species. We globally found similar results than previous ones obtained at catch level.

All BRT models showed that the catch level was better explained by the environmental conditions on free school (FS) than on FADs and that differences regarding environment-tuna distribution were more important among fish-sizes than among species. We quantified for the first time the dominant influence of the mesoscale in determining tuna's habitat, confirmed by the poor explanation obtained all "random catch" models, mainly for the IO. For both oceans, small-sized individuals were strongly related with the proximity to mesoscale eddies (<200 km) whereas the large-sized individuals were found at larger distances. A strong influence of mesoscale fronts was observed mainly in the EPO and in the IO only in the coastal regions where strong fronts become important.

In the EPO different environmental conditions were observed in three well defined sub-regions as the Peru coastal upwelling, the Equatorial Tongue, and the Costa Rica Dome. Contrarily to the IO were the fishing grounds were relatively homogeneous in terms of Sea Surface Temperature (SST) and Sea surface Concentrations (SSC) these two parameters highly influence the tuna distribution in the EPO. In general for the this ocean, the total percentage of relative contribution by category of variables for the entire modeling scheme was 34% for the mesoscale variables, 39% for the classic variables and 27% for the temporal and fishery-related variables. For the EPO, even if the mesoscale category remains important (37%), the most relevant contributions were found for the classic variables with 55% whereas 8% was observed for the temporal component.

Key words:

Tropical tunas, Indian Ocean, Eastern Pacific Ocean, mesoscale, purse seiner, species, fish-size, fishing mode.

Résumé

Le présent travail a pour but d'étudier la structuration à méso-échelle (de quelques kilomètres à quelques centaines de kilomètres) de l'environnement marin des trois espèces majeures de thons tropicaux, l'Albacore (Thunnus Albacares), le Listao (Katsuwonus Pelamis) et le thon obèse (Thunnus obesus), pêchés à la seine tournante dans les océans Indien (OI) et Pacifique Tropical Est (OPE), en fonction de leur tailles et leur distribution. Ces trois espèces représentent 20% et 13% des prises mondiales respectivement dans les 2 océans. Les prises par calées uniques sont utilisées pour évaluer séparément les caractéristiques environnementales de leur zones de pêche pour trois modes de pêche: sur banc libre (FS), objets flottants naturels (LOG) et sur dispositifs de concentration du poisson (DCP), ceci pour plusieurs tailles d'individus.

Préalablement aux analyses, une double méthode statistique et d'expertise est développée pour la détection et la classification des fronts thermiques à l'échelle des bassins océaniques, tandis que les tourbillons de méso-échelle, cycloniques et anticycloniques sont détectés par la méthode des winding angle. Les distances des prises à ces structures sont ensuite calculées. Nous trouvons que les fronts intenses concernent surtout les régions côtières et les fronts faibles l'océan du large, ce qui permet de considérer différents mécanismes de frontogénèse pouvant avoir un pouvoir attractif différent sur les thons.

En plus des composantes de méso-échelle, les variables classiques de l'environnement, les variables temporelles et celles liées à la pêche sont utilisées. Nous utilisons le modèle bayésien " Boosted Regression Tree" (BRT) suivant un schéma en 3 phases pour chaque océan pour explorer les réponses du niveau de prise pour chaque espèce et trois modes de pêche. La dominance relative est également explorée pour les prises sur FADs (O.I) et FADs et FS (OPE) et montre des résultats similaires au niveau de prise.

Tous les modèles BRT montrent que le niveau de prise est davantage relié à la variabilité environnementale pour les bancs libres que pour les FADs. Nous mettons en évidence pour la première fois l'importance des structures de méso-échelle sur la définition des habitats des thons, ce qui est confirmé par les faibles niveaux d'explication obtenus par les modèles de type « random », notamment dans l'océan indien. Pour les deux océans, les petits individus sont fortement associés aux tourbillons (distances <200km) tandis que les plus gros sont situés plus loin des tourbillons.

Une faible influence des fronts thermiques est constatée dans l'Océan Indien tandis que l'opposé est trouvé pour le Pacifique, sauf dans les régions côtières où les forts fronts ont une influence prépondérante. Différentes conditions environnementales sont observées dans des régions précises du Pacifique comme celles d'upwelling côtier, la bande équatoriale et le dôme du Costa Rica. Contrairement à l'Océan Indien, où les zones de pêches sont relativement homogènes en terme de température et chlorophylle de surface, ces deux paramètres influencent nettement plus la distribution des thons dans le Pacifique. Dans l'Océan Indien, le pourcentage total des contributions relatives par catégories de variables est de 34 % pour les variables de méso-échelle, de 39 % pour les variables classiques et de 27 % pour les autres (temporelles et pêche). Pour le Pacifique Est, même si la méso-échelle est importante (37%) les autres variables dominent en contribution relative (55 % et 8%).

Mots clé : Thons tropicaux, taille, Océan Indien, Océan Pacifique Est, environnement, méso-échelle, senne tournante, modes de pêche.

At this moment, when I am getting to the end of this PhD journey I truly believe that perseverance is the secret. If you continue working without giving up -despite the adversities-suddenly the magic happens, everything has a meaning and the right people appear around you. They are those that will help you day by day to reach your goal, they will -literally-put in your hands one by one of the strips of wood and the nails that you need to build the ship in which you and your fulfilled aim will be sailing. Once the ship finished, the satisfaction is not only to see how it raises on the sea but also to see all and each one of the sailors who will be navigating with you by the rest of your life. They are the same that had helped you building that beautiful ship, those that trusted in you and encourage you now to make the trip. To every sailor, thank you, without you this had not been possible. If I forget to mention someone here, that is not a problem, you know that your names are already registered in the "log book" and that we are ready to navigate! Before continue, I want to express my gratitude to the one that can do everything, the one that lifts you each time that you fall, the one that loves you in spite of your faults, the one that really knows your heart and to knows everything what you are capable to do, Thanks to God.

First and foremost I wish to thank my thesis advisor, Hervé Demarcq who trusted in me from the beginning. Thank you for your generosity sharing your knowledge, for your dedication, time and patience. Thanks a lot for supported me not only by providing a research assistantship over all these years, but also academically and emotionally through the rough road to finish this thesis.

Besides my advisor, I would like to thank all the members of the thesis committee, Doctor Daniel Gaertner thank you for your trust and your willingness answering all my questions, thank you for opening the doors to the collaborations done and for your recommendations. Doctor Alexis Chaigneau thank you for providing me with the oceanographic data derived from your useful methods and for your important remarks to this document. Doctor Alain Fonteneau thank you for your helpful comments and the very precise questions that improved this study. Doctor Jacques Panfili thank you for your time, willingness and your advice. I am especially grateful to Doctor Martin Hall, who arranged his agenda many times to be in France to support the development of this work and who provided me the opportunity to join their team at the Inter-American Tropical Tuna Commission (IATTC) as intern. Thank you for having accepted to be part of this study, for your trust, your very constructive comments, for being attentive to listen my questions and to open my mind to the specific details of the fisheries in the Pacific Ocean. I want to thank you also for motivating me to meet new researchers and to know about new oceanographic methods. Thank you for encouraging me to present my results everywhere (Colombia, USA and France). Thank you for having introduced me to the people at NOAA and ESR labs. Finally thanks for teaching me to believe in my ideas and skills and for always consider my suggestions.

My sincere thanks also to the director of the IATTC Doctor Guillermo Compean, who approved the collaboration proposed and my stay in La Jolla. Also thanks to all IATTC staff for the collaboration, specially to Marlon Roman, Nick Vogel, Carolina Minte Vera and to Marisol Aguilar. Also thanks to the people whom I had the pleasure to share with during in California, Karen, Gaston, Xochilt, Alain, thanks for the warm environment, for providing me a nice home and for your friendship.

I gratefully acknowledge Doctor Francisco (Cisco) Werner and Doctor Paul Fiedler, from NOAA, for their time and important comments and suggestions. Also thanks to Doctor Yi Xu for your advice about statistical modeling. I am also grateful to Doctor Kathleen Dohan who gave me the opportunity to work with her at ESR in Seattle, for your time and willingness, for teaching me the functioning of OSCAR model, also thank you very much for your advice and perspectives for the future.

In Sète I want to thank the Observatoire thonier, mainly to Pierre Chavance, Emmanuel Chassot and Pascal Bach for the access to the data bases and for funding the final months of my PhD. Also thanks to Laurent Floch, Pascal Cauquil and Alain Damiano for the data, for the interviews with the observers and the explanations about the dynamics of the french purse seine tuna fleet.

An special acknowledgment goes to my great friends and colleagues, Doctor Karen Nieto thanks for your sense of humor, your positive energy, motivation and your strong technical collaboration. To Isabel Terrier, Isa thanks for your kindness, for sharing your home with me several times, in Sète and in Paris, for your kind words, your trust and overall for your interest to keep alive our friendship despite the distance . Also thanks to my office mate of all these years, Doctor Sibylle Dueri a true friend ever since we began to share an office. Thank you for the nice conversations, for sharing tips about maternity, for your big heart, for your autonomy, discipline and dedication to work that I deeply admire. I also want to thank to my old friend Paola Sandoval -part of our "Marine Biology's soccer team" in Colombia-for be my sister in France, to had encouraged me to continue and for remembered me who I was and who I have to continue being now. Thanks to Manuela Capello for the motivation and support for the afternoon "promenade" with our babies. I thank with all my heart to all these valuable women for being with me during the most difficult moments. I want to express my sincere gratitude to my good friend Norbert Billet and his family to be the support needed in the last and most critical moments of this PhD journey. Thanks for loving my daughter, Mana, for letting her share with you and for being her babysitter on weekends. Thank you for the warm food when I didn't have time to cook, for providing us a warm home in winter, for the confidence, the company and the moral support. Thanks a lot for many more things very difficult to define -because their value is difficult to quantify!-. Thanks a lot for your selflessness friendship, in a few words thanks for being our family in Sète.

I warmly thank to the Unit of research EME, today MARBEC, I'd like to start to thank the directors Doctor Philippe Cury (EME) and Laurent Dagorn (MARBEC), who had welcomed and had supported this

Agradecimientos

"No conocemos a las personas por accidente, ellas se cruzan en nuestro camino por una razón" (Anonimo).

En este momento, cuando llego al final de esta etapa, creo firmemente que en la perseverancia esta el secreto. Si continuas sin desfallecer -a pesar de las adversidades-sin darnos cuenta llega ese momento mágico en que lo necesario ocurre y las personas indicadas aparecen a tu alrededor. Son estas personas las que te ayudaran a alcanzar tu meta, ellas te irán pasando -literalmente-uno a uno los listones de madera y los clavos que necesitas para construir al barco en el que navegaras con tu objetivo cumplido. Una vez terminado, la verdadera satisfacción, ademas de ver como su imponente figura se alza sobre el mar, es ver cada a uno de los marineros que navegaran contigo por el resto de tu vida. Ellos son los mismos que te ayudaron a construir el barco, que confiaron en ti sin desfallecer y te animan ahora a emprender el viaje. A todos y cada uno de ellos, gracias, sin ustedes esto no hubiera sido posible, si olvido mencionar algún nombre eso no es un problema, ustedes saben que todos están inscritos hace tiempo en la bitácora de navegación!.

Primero que todo quiero agradecer al que todo lo puede, al que te levanta cuantas veces te tropieces y caigas, al que te ama a pesar de tus defectos, al que conoce tu corazón y sabe de todo lo que eres capaz, Gracias a Dios.

Mi mas sincero agradecimiento a mi director de tesis Hervé Demarcq, quien confió en mi desde el inicio. Gracias por su generosidad al compartir su conocimiento, por su tiempo, dedicación y paciencia. Muchas gracias por apoyarme todos estos años no solo en investigación sino academicamente y emocionalmente especialmente a través del difícil camino hacia el final de la tesis.

Quiero agradecer también a todos los miembros del comité de tesis, al Doctor Daniel Gaertner por la confianza, gentileza y buena disposición para responder mis preguntas, gracias por abrir las puertas a las colaboraciones hechas, por los contactos y las recomendaciones. Al Doctor Alexis Chaigneau gracias por la colaboración con los datos oceanográficos derivados de sus propios e interesantes métodos. Al Doctor Alain Fonteneau gracias por sus comentarios y muy precisas observaciones útiles para mejorar este trabajo. Al Doctor Jacques Panfili gracias por su tiempo, buena disposición y sus consejos. De manera especial quiero agradecer tambien al Doctor Martin Hall, quien re-organizo su agenda muchas veces para estar en Francia y acompañar el desarrollo de este trabajo y quien me dio la oportunidad de unirme al equipo de la Comisión Interameticana del Atún Tropical (CIAT) durante mi estancia de investigación. Gracias por aceptar ser parte de este estudio, por confiar en mi y por sus comentarios siempre muy constructivos. Gracias por escuchar mis preguntas de manera atenta y por abrir mi mente a los detalles mas interesantes de la pesca en el Océano Pacifico. Igualmente gracias por motivarme a conocer nuevos investigadores, nuevos sistemas de trabajo y nuevos métodos oceanográficos en USA. Gracias por animarme a presentar mis resultados en Colombia, USA inclusive en Francia. Gracias por presentarme ante la NOAA y ante el ESR. Finalmente gracias por enseñarme a creer en mis ideas y mis capacidades y por siempre valorar mi criterio y considerar mis sugerencias.

Quiero aprovechar la oportunidad para agradecer al Doctor Guillermo Compean, director de la CIAT, quien valido la colaboración propuesta y mi estancia allí. Gracias también al resto del staff CIAT por su bienvenida y colaboración especialmente a Marlon Roman, Nick Vogel, Carolina Minte Vera y Marisol Aguilar. Gracias a las personas con las que compartí en California, fuera de la CIAT, como Karen, Gaston, Clarita, Xochilt, y Alain, gracias por brindarme un agradable y cálido lugar para vivir y por su valiosa amistad.

Gracias al Doctor Francisco (Cisco) Werner y al Doctor Paul Fiedler de la NOAA por su tiempo y sus comentarios y sugerencias. Gracias también a la doctora Yi Xu por la asesoría con los modelos estadísticos. Estoy muy agradecida con la Doctora Kathleen Dohan quien me dio la oportunidad de trabajar en su oficina en ESR en Seattle, gracias por su tiempo y buena disposición para enseñarme el funcionamiento del modelo OSCAR. Finalmente gracias por sus consejos y perspectivas a futuro.

En Sète quiero agradecer al Observatoire thonier, en especial a Pierre Chavance, Emmanuel Chassot y Pascal Bach, por el acceso a las bases de datos y el financiamiento en los últimos meses de doctorado. Muchas gracias a Laurent Floch, Pascal Cauquil y Alain Damiano, por proveerme los datos requeridos, por el contacto con los observadores a bordo y por las explicaciones sobre la dinámica de la flota pesquera francesa.

Un agradecimiento muy especial a mis grandes amigas y colegas, Doctora Karen Nieto (de nuevo), gracias por tu sentido del humor y la buena energía, por la motivación, por el apoyo técnico y por supuesto las largas charlas entre "madresss". A Isabel Terrier, por el cariño, por compartir varias veces tu casa durante el verano en Sète y después en París, por los buenos consejos, por la confianza, por el interés en conservar la amistad a pesar de la distancia. A la doctora Sibylle Dueri, una verdadera amiga desde el día que comenzamos a compartir nuestra oficina. Gracias por las buenas conversaciones, por tu autonomía, disciplina y dedicación al trabajo que tanto admiro. Gracias también por compartir tips sobre maternidad, por tu gran corazón!. Igualmente gracias a mi amiga de tanto tiempo Paola Sandoval, -co-equipera de fútbol en la selección de Biología Marina en Colombia-por ser mi hermana defensora en Francia, por darme el coraje necesario para levantar la cabeza, recordarme quien era en ese entonces y quien debo seguir siendo siempre para poder seguir hacia adelante. Gracias también a Manuela Capello por la motivación y por los "paseos de fin de tarde" con nuestros bebes. Agradezco con todo mi corazón a todas estas valiosas mujeres por estar conmigo durante los mas difíciles momentos, alegrándome la vida.

Un infinito y sincero agradecimiento a mi buen amigo Norbert Billet y a su familia por ser el soporte necesario en los días y noches críticos, por querer y cuidar a Maná, mi hija. Gracias por la comida caliente cuando no tenia tiempo para cocinar, por ser la "niñera" de los fines de semana, por brindarnos un hogar Ademas de las personas mencionadas, quiero a los gobiernos colombiano y francés por ser las fuentes de financiación durante el doctorado: al Instituto Colombiano de Crédito y Estudios Técnicos en el Exterior (ICETEX), a la Fundación COLFUTURO, al Institut de Recherche pour le Développement (IRD) y al Observatoire Thonier (IRD, UMR MARBEC).

Finalmente y de manera muy importante quiero dedicar este trabajo a mi familia, a mis padres, a mis hermanos y a mi hermana, a ustedes que son mis mejores amigos. Gracias por que siempre me han apoyado y han confiado ciegamente en mis sueños, muchas veces solo llevados por mi entusiasmo. Gracias a Juanito, mi padre, por creer e invertir en mi, por darme las alas que necesitaba en medio de una época convulsionada para nuestro país, donde lo mas importante era aprender a sobrevivir antes que aprender a soñar. Gracias a Edna, mi madre, porque su ejemplo me hizo valorar mi condición de mujer libre-pensante, honesta, capaz y disciplinada, herramientas útiles para enfrentar un mundo lleno experiencias nuevas y de culturas diferentes. Gracias a mis hermanos, Margarita (Nenita y a Omar) y Andrés por ser mi "polo a tierra", sus consejos, su apoyo moral y económico me ayudaron a estar donde estoy. Gracias a Yova y a Dieguito (y Amparo) por las risas, por hacerme cuestionar siempre sobre los objetivos en mi vida y por siempre impulsar mis decisiones con valentía y mucho humor. Gracias a mi sobrina Cami por su tierno interés y por compartir París conmigo!. Gracias a mi hija Manuela "Tuti" por ser ese faro que ilumino mi camino cuando todo se hizo completa oscuridad. Gracias mi ángel por mostrarme la fuerza que nunca pensé que tenia ni que era capaz de desarrollar. Gracias por enseñarme el material del que están hechos los buenos seres humanos. Gracias por enseñarme de que se trata el amor verdadero pero lo mas importante, gracias por escogerme como tu mamá. SST with the main currents (current abbreviations as in Figure 1) and occurrence of thermal fronts of (c) weak and (d) "strong" intensity.

D. Environmental description of the fishing grounds in the

Introduction

L'étude des vastes écosystèmes marins pélagiques, pour certains presque inconnus, devient particulièrement intéressante lorsqu'il s'agit de comprendre les interactions entre les espèces et leur environnement. Dans ce cadre, il est important de considérer les processus physico-chimiques qui s'y déroulent et qui jouent un rôle essentiel dans la distribution des espèces en structurant leur environnement, comme les processus de meso-échelle, dont on citera les fronts, les tourbillons et certains courants, pour les plus importants. Ce niveau de structuration permet de compléter la définition d'habitat des espèces, comme celles, très importantes d'un point de vue écologique et économique, que sont les thons tropicaux.

La température, la vitesse du vent, la salinité et la concentration en pigments chlorophylliens sont les variables les plus fréquemment utilisées pour caractériser l'environnement océanique. De leur variabilité dérive largement les processus physiques qui y ont lieu, à différentes échelles spatiales et temporelles. Par exemple, les caractéristiques bio-géochimiques à l'échelle régionale sont influencées par la méso-échelle (fronts et tourbillons) qui augmentent ou réduisent la productivité locale et par conséquent la disponibilité des proies des thons. De plus, les dynamiques de méso-échelle influencent les variables biologiques telles que la croissance des espèces marines. La compréhension de ces interactions bio-physiques est la clé pour caractériser un habitat et pour identifier les conditions environnementales qui déterminent la distribution d'une espèce donnée.

Dans les études sur la distribution des thons tropicaux, la SST est la variable environnementale la plus utilisée [START_REF] Andrade | Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast[END_REF][START_REF] Barkley | Skipjack tuna, Katsuwonus pelamis, habitat based on temperature and oxygen requirements[END_REF][START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF][START_REF] Stretta | Forecasting models for tuna fishery with aerospatial remote sensing[END_REF][START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF]. Par exemple, [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] à partir des données de longline du Sri Lanka (NE Océan Indien ) ont trouvé que de fortes prises d'Albacore dans des régions où la SST varie de 28°C à 30°C. Dans une autre étude, [START_REF] Hanamoto | Effect of oceanographic environment on Bigeye tuna distribution[END_REF]) a noté que le thon bigeye a une distribution précise en profondeur (entre 0 m-600 m), différente des autres espèces de thon, avec un optimum thermique qui varie entre 10°C-15°C et 10°C-18°C. La température est aussi un paramètre clé dans les modèles statistiques utilisés pour prédire la présence de thon Listao (Katsuwonus pelamis (Linnaeus, 1758)) dans les scénarios où différents engins de pêche sont utilisés. [START_REF] Barkley | Skipjack tuna, Katsuwonus pelamis, habitat based on temperature and oxygen requirements[END_REF][START_REF] Evans | Propagation of thermal fronts in the somali current system[END_REF]Andrade & Garcia 1999a;Dueri et al. 2012) utilisent la température de surface comme un indicateur de tolérance du thon Listao face aux changements environnementaux.

La salinité est connue pour son influence particulière sur la distribution des thonidés en général, en particulier sur la distribution du thon obèse (bigeye tuna) (Song & Gao, 2006). Un autre exemple pour cette même espèce est l'utilisation de la vitesse du vent par [START_REF] Pereira | Behaviour of bigeye tuna in a baitboat fishery[END_REF] qui a observé une forte relation entre la vitesse du vent entre 0.5 m/s et 5.4 m/s et le nombre d'événements de pêche et leur prise associée. D'autres paramètres importants sont la chlorophylle de surface (SSC or Chl-a) et la production primaire associée. [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] a trouvé une forte association entre Chl-a (valeurs entre 0.1-0.4 mg.m -3 ) et les prises d'Albacore. En général, la recherche actuelle montre que de fortes valeurs de Chl-a sont généralement associées à de bonnes prises; cependant, la dynamique de cette relation est complexe car indirecte et varie beaucoup d'un écosystème à l'autre car les thons adaptent leur alimentation et leur stratégie de survie à leur milieu.

Les structures de méso-échelle influencent fortement la distribution locale des thons tropicaux et par conséquence la structuration spatiale des zones de pêche. En effet, ils peuvent affecter les caractéristiques biogéochimiques locales, par exemple en augmentant la productivité primaire et par conséquent la disponibilité des proies recherchées. Définies comme les frontières entre deux masses d'eau de nature différente, les fronts sont des structures très communes de la surface océanique à des échelles spatiales entre 1 km et 1000 km [START_REF] Fedorov | The physical nature and structure of oceanic fronts[END_REF]. Les zones frontales sont souvent très dynamiques, agissant souvent comme transfert d'énergie des grandes vers les petites échelles [START_REF] Ullman | Satellite-derived sea surface temperature fronts on the continental shelf off the northeast U.S. coast[END_REF]. Quand un front se développe (par des procédés liés à la fronto-génèse), une zone de convergence se crée entre les deux masses d'eau. Des nutriments peuvent se concentrer dans cette zone et, suivant la persistance du front, peuvent donner lieu à une production primaire importante.

Les tourbillons de méso-échelle sont également très courants. Au contraire des fronts, ils ne sont pas présents au voisinage de l'équateur ( 5°N and 5°S) à cause de l'absence de la faiblesse de l'accélération de Coriolis. Ils jouent cependant un rôle important dans la répartition du bilan thermique océanique et dans la circulation océanique [START_REF] Siegel | Bio-optical footprints created by mesoscale eddies in the Sargasso Sea[END_REF]. Ils favorisent le transport vertical de nutriments et en conséquence augmentent ou réduisent la productivité en surface et sub-surface de l'océan. En fonction de leur sens de rotation, ils sont de type cyclonique or anticyclonique. Un tourbillon anticyclonique est associé à un flux de surface convergent en son centre, plus chaud et pauvre en nutriments.

La présente étude est d'abord basée sur la détection et la classification des fronts thermiques présents à la surface de la mer, ce qui est motivé par l'abondance d'études montrant leur importance sur la présence locale des thons. Nous considérons en particulier l'intensité du gradient local associé au front comme une indicateur possible de leur attractivité pour les thons.

Cette étude est basée sur l'analyse des prises des thons tropicaux à la seine tournante dans l'Océan Indien et l'Est Pacifique par les différents modes de pêche, sur banc « libre », sur objet flottant naturel ou via des objets flottants artificiels dédiées, de plus en plus utilisés par ces pêcheries, que sont les DCP (Dispositifs de concentration de poissons), en anglais FAD (Fish aggregating device) et leurs interactions avec l'environnement, en particulier à méso-échelle.

A travers cette étude, nous visons trois objectifs :

1. La description et la quantification des structures thermiques et topographiques détectables sur l'imagerie satellitale (fronts thermiques et tourbillons), connus pour leur attractivité potentielle 2. La description des habitats des thons à travers des descripteurs généraux :

• SST (température de surface de la mer)

• SSC (concentration de la chlorophylle de surface) et de sa dynamique temporelle • MLD (profondeur de la couche de mélange)

• oxygène dissous dans la couche de surface [0-50m]et de méso-échelle :

• EKE (énergie cinétique tourbillonnaire)

• Distance aux fronts thermiques (de deux types en fonction de leur gradient)

• les variables temporelles : saison et année 3. La comparaison des régions étudiées du point de vue de la réponse des thons à l'ensemble de ces variables

Régions d'étude Océan Indien et Océan Pacifique Est

Les caractéristiques océanographiques de l'Océan Indien (IO) diffèrent fortement de celles de l'Atlantique et du Pacifique, principalement parce qu'il est considéré comme un océan « fermé », bordé au Nord par le continent asiatique. Le contraste thermique entre terre et mer crée une renverse de vent saisonnière et une profonde variabilité des principaux courants [START_REF] Fieux | Current system in the Indian ocean[END_REF], qui affectent en particulier la mer d'Arabie, la péninsule indienne et la baie du Bengale, localisée au Nord du bassin de chaque coté de la péninsule indienne [START_REF] Fieux | Current system in the Indian ocean[END_REF]. Les nombreuses structures spécifiques des courants de surface génèrent une importante activité frontale et tourbillonnaire, rendant l'Océan Indien un très bon candidat à l'étude de la dynamique de l'activité méso-échelle [START_REF] Roa-Pascuali | Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application[END_REF]. La limite sud-ouest du bassin est modifiée par la présence de Madagascar et du canal du Mozambique, tandis qu'à l'Est, l'Océan Indien est connecté à l'Océan Pacifique équatorial par le profond passage avec les mers indonésiennes.

Contrairement à l'Océan Indien, l'Océan Pacifique Est est un océan « ouvert » du nord au sud, sans continent qui restreint sa circulation et les échanges thermiques avec les hautes latitudes. A cause de la forte variabilité spatiale du forçage océanique océan-atmosphère [START_REF] Amador | Atmospheric forcing of the eastern tropical Pacific: A review[END_REF]) l'EPO présente un environnement unique et diverse pour l'étude des processus bio-physiques qui s'y déroulent et de leur impact sur la chaîne trophique [START_REF] Lavín | A review of eastern tropical Pacific oceanography: Summary[END_REF].

Le rôle du transport d'Ekman et de ses effets sur le mélange vertical de l'EPO sont bien observés à travers la distribution de paramètres clé comme la température et la salinité de surface, la profondeur de la thermocline et de la couche de mélange [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF]. Simultanément, les structures de méso-échelle, comme les fronts, les tourbillons et les méandres sont générés par le pompage d'Ekman [START_REF] Willett | Eddies and Tropical Instability Waves in the eastern tropical Pacific: A review[END_REF]) des upwellings côtiers et des mouvements verticaux du large. Additionnellement, la topographie de la thermocline est reliée à l'effet de la balance géostrophique et des ondes de Kelvin [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF].

L'atmosphère de l'EPO est à la source d'un phénomène rare : la présence de trois étroits jets côtiers soufflant du continent vers le large : celui de Tehuantepec, du Papagayo et du Panama. Bien qu'intermittents de Novembre à Avril, leur effet est très fort sur la température et la chlorophylle de surface.

Comparaison à grand échelle des Océans Indien et Pacifique Est

• L'Océan Indien est bordé au Nord par le continent asiatique, empêchant le transfert de chaleur vers les hautes latitudes et réduisant la ventilation de la thermocline.

• Le continent asiatique force la plus forte mousson de la planète dont le vent associé génère d'intenses variations saisonnières des courants associés, dont le renversement de certains comme le courant de Somalie et le courant de mousson SW/NE du Sud de l'Inde et du SriLanka.

• Il n'existe pas d'upwelling équatorial dans l'est de l'Océan Indien, au contraire des autres océans.

• Des upwelling locaux se produisent dans l'hémisphère Nord au large de la péninsule Arabique et de chaque coté de l'Inde et dans l'hémisphère sud le long de la limite nord des alizés de SE.

• L'upwelling de Somalie est alimenté par une cellule cross-équatoriale (CEC) qui n'existe pas non plus dans les autres océans et qui transporte une partie de la chaleur à travers l'équateur.

• L'Océan Indien reçoit par contre de la chaleur du Pacifique équatorial à travers le « throughflow » indonésien.

• La présence de la zone Inter-topicale de convergence (ITCZ), toujours positionnée dans l'hémisphère Nord, oscille entre 5°N en hivers et 10°N en été et constitue une autre particularité de l'Océan Indien.

• La présence de la « warm pool » pacifique, une province biogéographique ouverte avec une communauté biologique bien définie.

• La plus importante caractéristique : la thermocline forte et peu profonde de l'océan Pacifique Est.

• La présence de vent fort sur des régions étroites et soufflant de la terre vers la mer, détectable à travers la température de surface et la chlorophylle. Différents type de prises sont effectués durant les opérations de pêche en fonction du comportement des thons. Les thons sont connus pour migrer sur de grandes distances mais en même temps s'associent avec d'autres espèces ainsi que sur différents objets flottants. Ces associations définissent différents types de prise: sur bancs libres (thons non associés), ou associés à des dauphins, des monts sous-marins, des baleines, des troncs d'arbre, des Dispositifs de concentration de poissons (DCPs), etc. [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. Les quatre types les plus fréquents dans l'Océan Indien et l'EOP sont :

Les pêcheries à la seine tournante

• Les prises sur bancs libres, où les navires détectent une perturbation de la surface de l'Océan indiquant la présence de thons, parfois associée à celle d'oiseaux. Cela se produits durant un comportement de prédation ou d'autres comportement actifs en surface. Ce type de prise correspond à un comportement des thons non lié à une association avec un objet en surface. • Les opérations de pêche à la seine tournante consistent en trois opérations: 1) la recherche d'agrégations de thons à l'aide de radars à oiseaux et l'observation à la jumelle pour évaluer la taille et la capturabilité des bancs 2) la seine est déployée à laide d'un navire auxiliaire 3) elle entoure le banc détecté puis est refermée sous le banc et hissée en tirant sur un câble qui resserre les anneaux de la partie inférieure.

Description des thons tropicaux

Les thons ont la particularité unique parmi les téléostéens de pouvoir élever leur température musculaire au dessus de celle de l'eau (Care et al, 1971;Graham, 1975[START_REF] Block | Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range[END_REF]. Par rapport aux autres espèces, les thons ont un mode de nage et une régulation thermique radicalement différents, des fonctions accélérées (taux métabolique standard (SMR), capacité aérobique, rythme cardiaque et clairance intestinale) ainsi qu'une physiologie cardiaque spécifique [START_REF] Graham | Tuna comparative physiology[END_REF]. Ces caractéristiques physiologiques donnent aux thons tropicaux des capacités migratoires importantes ainsi que des aires de répartition vastes. [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF] ont décrit le thon Albacore comme une espèce cosmopolite pan-tropicale distribuée dans les régions chaudes et tempérées. Des études conduites dans le Pacifique Est et Central par Carey andOlson (1982) et Holland et al. (1990) indiquent que les mouvements verticaux de l'Albacore sont principalement réduits à la couche de mélange de surface jusqu'à la partie supérieure de la thermocline. D'autres études indiquaient que l'Albacore peut atteindre occasionnellement une profondeur de 460 m sur de courtes périodes. La préférence pour la couche de mélange a aussi été observée pour les jeunes individus, à des températures de 19°C à 26°C [START_REF] Block | Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range[END_REF].

Le Listao est principalement présent entre 24°C et 32°C dans les trois océans. Les écosystèmes équatoriaux et leurs courants chauds associés, tels le que Kuroshio et le courant du Brésil, représentent leur habitat favori [START_REF] Fonteneau | A comparative overview of skipjack fisheries and stocks world wide[END_REF][START_REF] Dueri | Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution[END_REF], bien qu'un faible pourcentage de Listao se retrouve entre 18 et 24 °C comme en nouvelle Zélande et aux Azores [START_REF] Dueri | Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution[END_REF]. Cette espèce ne suit pas de migration de ponte ou de nutrition [START_REF] Stéquert | Reproduction of skipjack tuna(Katsuwonus pelamis) from the western Indian Ocean[END_REF] mais plutôt des mouvements au sein de leur habitat préférentiel, en fonction des conditions environnementales comme la disponibilité de leur proies, la température et l'oxygène dissout qui affectent leur distribution, déplacements et capturabilité (Dueri et and Maury, 2012).

Le thon obèse est largement présent dans les trois océans entre 45°N et 40°S [START_REF] Laevastu | Distribution and relative abun-dance of tunas in relation to their environment[END_REF][START_REF] Kikawa | Maturation and spawning of tunas of Indian Ocean[END_REF][START_REF] Collette | FAO Species Catalogue. Vol. 2. Scombrids of the world. An annotated and illustrated catalogue of tunas, mackerels, bonitos and related species known to date. Rome: FAO[END_REF][START_REF] Carocci | Pacific tunas and billfishes: atlas of commercial catches. Food and Agricultural Organization[END_REF]Fonteneau et al., 2004;[START_REF] Lee | Spatial and Temporal Distribution Patterns of Bigeye Tuna (Thunnus obesus) in the Indian Ocean[END_REF]. Leur distribution eurybathymétrique rend plus difficile de relier leur distribution horizontale et leur déplacements à des conditions environnementales de surface. L'aptitude physiologique des adultes à réguler leur température (Graham and Dickson, 2001) et leur tolérance à de bas niveaux d'oxygène dissous (Brill, &994) leur permet de coloniser des eaux profondes durant la journée et de se nourrir de proies méso-pélagiques dans la riche couche diffusante (sound scattering layer, SSL) [START_REF] Josse | Simultaneous observations of tuna movements and their prey by sonic tracking and acoustic surveys[END_REF]Dagorn et al, 2000;[START_REF] Bertrand | Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability[END_REF]Bach et al, 2002;[START_REF] Maury | APECOSM : a spatialized, physiologically structured apex predators ecosystem model[END_REF].

Données et Méthodes

Les données de pêche de l'Océan Indien (IO)

Méthode d'échantillonnage multi-spécifique

La méthode d'échantillonnage multi-spécifique est une procédure appliquée aux données des thoniers senneurs depuis 1980 dans l'Océan Atlantique, depuis 1983 dans l'Océan Indien et depuis 2000 dans l'Océan Pacifique, avec un taux d'observateur à bord qui atteint 100% pour ce dernier. La composition spécifique de toutes les prises des senneurs dans l'Indien et le Pacifique ont été corrigées par cette méthode, qui consiste à déterminer la composition spécifique et par taille des prises à moyenne échelle à partir des débarquements (Fonteneau 2007). L'objectif de la méthode est de corriger le biais observé sur les prises par espèces dans les rapports déclarés par les capitaines, en particulier pour les petites tailles de yellowfin et de bigeye [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF].

La base de données "SPECIES"

Les données des senneurs dans l'Océan Indien (IO) sont issues de la base SPECIES (Somme des Poids Échantillonnées par CatégorIES), obtenue grâce au programme « Tuna Observer » de l'IRD (Unité MARBEC, Sète France). Cette base (Fonteneau et al 2007) dérive de l'échantillonnage original des espèces par taille [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF]. Elle établi pour chaque échantillon un résumé des observations par espèce, classe de taille et composition spécifique (juvénile et adulte de yellowfin et bigeye) ainsi que le poids, dans chaque échantillon, pour les 3 espèces débarquées. Les données sont extraites pour la période 2003-2010.

Données de pêche pour l'Océan Pacifique Est (EPO)

Les prises par callée ne comportant qu'une seule espèce (single sets) de l'EPO ont été fournies par l'IATTC (Inter-American Tuna Commission), par un agrément scientifique avec l'UMR EME de l'IRD. Les données sont corrigées par espèce et composition, comme mentionné plus haut (voir section 4.1.1) et correspondent à certaines zones mentionnées déclarées par les observateurs (voir Figure 4.12). La période d'étude correspond aux prises de 2007 et 2008, années sans événement El Niño et seulement par « La Niña » moderée en 2008 (Figure 4.13), soit des années « normales » d'un point de vue climatique.

La base de l'IATTC contient l'identification des coups de seine, leur localisation, la date, la composition spécifique ainsi que la taille, le poids et le mode de pêche. La Figure 4.14 montre l'abondance relative des 3 espèces (YFT, SKJ et BET) dans l'EPO en 2007 et 2008.

Données satellite

En dehors des variables "classiques" de surface, un travail particulier a été mené sur les paramètres dérivés de la méso-échelle comme les fronts et tourbillons, ces derniers étant été souvent associés à des concentrations de thons (voir Introduction). C'est pourquoi une méthodologie spécifique de détection frontale a été développée pour calculer les fronts thermiques à l'échelle d'un bassin océanique entier (méthode décrite dans le chapitre 5)

• Température de surface de la mer (SST) et chlorophylle a de surface (SSC)

• Énergie cinétique tourbillonnaire (EKE)

• Bathymétrie (Bathy)

• Vitesse du vent (Ws)

• Dynamique de la chlorophylle a de surface (SSC_Dynm)

• Profondeur de la couche de mélange (MLD)

• Oxygène Dissous (Disslv. Oxygen)

• Distance aux tourbillons (Dist. Eddies)

• Distances aux fronts (Dist. Fronts)

Agrégations spatiales de prises

Nous avons déterminé qu'un repositionnement aléatoire des prises dans un rayon de 250 km était nécessaire pour détruire l'agrégation spatiale des données. Il est intéressant de noter que cette distance de dispersion est proche de la taille moyenne des structures de méso-échelle océaniques courantes, dominées par les tourbillons, sauf près de l'équateur. L'hypothèse « la structuration à petite échelle des prises est dépendante des structures océaniques de méso-échelle » est testée par l'utilisation de variables qui décrivent spécifiquement ces structures, en particulier la distance depuis le bord des tourbillons et les fronts thermiques.

Afin de tester l'importance de ces variables, les données de capture sont dispersées de manière aléatoire dans un rayon maximal de 250 km, correspondant environ à la taille moyenne des tourbillons, principales structures de méso-échelle.

Extraction des données de fronts thermiques

Afin d'optimiser la détection des fronts thermiques à partir des champs de SST à 4km de résolution, une approche statistique est appliquée aux résultats du lissage spatial des données avant l'application de l'algorithme de détection. Les fronts sont habituellement détectés sur des données de SST à la résolution d'un kilomètre développé par l'algorithme de Cayula et Cornillon en 1992, précédé d'un lissage conventionnel par un filtre médian de 3×3 pixels. Ici, les détections sont testées dans trois régions (Maroc, Canal du Mozambique et Australie NW) ainsi qu'à l'échelle de l'Océan Indien par l'utilisation combinée de la méthode "CMW" développée par Nieto et al. en 2012 et qui utilise l'algorithme original de Cayula et Cornillon. Nos détections à 4 km sont ensuite comparées à celles habituellement obtenues à 1 km de résolution.

Les fronts sont séparés en deux classes d'intensité ("faibles" et "forts") en fonction de leur gradient thermique. Des tests de lissage préliminaire sont appliqués avant la détection pour trois convolutions différentes (median, average and Gaussian) combinés à 4 intensités de lissage c'est à dire de taille de kernel (3×3, 5×5, 7×7, et 9×9 pixels) ainsi qu'à 3 tailles de fenêtres de détection (16×16, 24×24 et 32×32 pixels) afin de tester les effets de ces combinaisons sur la réduction du bruit des données et par conséquent sur la performance de la détection. Celle-ci est évaluée par deux critères: l'efficacité de la détection et la longueur (et continuité) des fronts résultants.

Les résultats montrent qu'une solution stable et générique est trouvée pour un filtre de type médian et une taille de fenêtre de détection de 16x16 pixels, combinée à une taille de kernel de 5×5 pixels pour les fronts forts et de 7×7 pixels pour les fronts faibles. L'amélioration de la détection de la plus grande à la plus petite taille de fenêtre est de 71% pour les fronts forts et de 120% pour les fronts faibles. En dépit de la faible taille de fenêtre (16×16 pixels), nombre et longueur moyenne des fronts sont préservés par rapport à ceux détectés à partir de donnée à 1 km de résolution , 16 fois plus coûteuse en temps de calcul.

Distances aux tourbillons (Dist. Eddies)

Les caractéristiques des tourbillons (localisation, diamètre et durée de vie) ont été fournies par Alexis Chaigneau, chercheur physicien de l'IRD qui a mis au point une méthode de détection des tourbillons par "Winding Angle" [START_REF] Chaigneau | Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns[END_REF].

Le calcul des distances des points de pêche aux tourbillons les plus proches, anticycloniques ou cycloniques a été calculée à partir du bord des tourbillons et des cartes de distribution de ces distances ont été réalisées. Il faut noter que les quelques tourbillons détectés dans la zone de 5°N à 5°S ne sont pas considérés car très exceptionnels.

Analyse statistique

Nous présentons ici les analyses multivariées de l'identification des conditions environnementales associées aux prises des thons. Un schéma de modélisation a été élaboré en 3 sections, en fonction des 3 variables de réponse aux 12 variables d'environnement examinées. Les variables réponse sont: 1) la localisation des prises (transformées en log décimal), 2) la location aléatoire des prises décrites plus haut, 3) la dominance relative de chaque espèce capturée sur FADs (Fig. 6.1). L'analyse est divisée en 4 groupes de prédicteurs:

• Variables générales (continues): Bathymétrie (Bathy), température de surface (SST), chlorophylle a de surface (SSC), dynamique de la chlorophylle de surface (SSC Dynm), vitesse du vent (WS), épaisseur de la couche de mélange (MLD) et oxygène dissous (DO);

• Variables de méso-échelle: -Énergie cinétique tourbillonnaire (EKE), distance aux fronts faibles (Dist. W. fronts), distance aux fronts forts (Dist S. fronts), distance aux tourbillons anticycloniques (Dist A. eddies) et cycloniques (Dist. C. eddies);

• Variables de pêche: -Mode de pêche, capacité du navire (Vess. cap) et année de mise en service (Vess. Year). Ces 2 dernières variables ne s'appliquent que pour l'Océan Indien.

•

BRT model approach

Le modèle BRT est un ensemble de méthodes qui combine la puissance des modèles d'arbres de régression (qui relie une réponse à des prédicteurs par partages binaires récursifs) et le « boosting model », qui est une méthode adaptative de combinaison de nombreux modèles simples pour augmenter la performance de prédiction. Cette approche combinée rend le BRT différent des techniques conventionnelles de modélisation unique et parcimonieuse. Le modèle BRT final correspondrait à un modèle de régression additive dont les termes individuels sont de simples arbres de régression ajustés pas à pas [START_REF] Elith | A working guide to boosted regression trees[END_REF].

Schéma de modélisation

Nous créons un schéma de modélisation basé sur une série de modèles BRT construits et ajustés avec les packages R 'dismo ' (Hijmans et al. 2015) et 'gbm' (Greg Ridgeway, 2015). La première section du schéma, nommée "modèles de prises", inclus les modèles utilisant la variable réponse « prise » ajustée en log. La deuxième section du schéma, dite "modèles de prises aléatoire", regroupe les modèles où prédicteurs et variables environnementales dérivent de positions aléatoires au sens de la méso-échelle. La troisième section, des "modèles de dominance", inclus les analyses de dominance spécifique des prises sur DCPs.

Le schéma de modélisation comporte quatre groupes de prédicteurs : les variables classiques, de mésoéchelle, celles relatives à la pêche et enfin les variables temporelles (Table 6.1). Ces prédicteurs varient en fonction des caractéristiques de chaque modèle. La corrélation de Pearson entre prédicteurs est vérifiée avant la construction du schéma.

Les jeux de données considérés comme importants (n>1000) sont ajustés avec un jeu de données d'entraînement (60% des données) et de test (les 40% restants). Cette étape précise le degré total de performance du modèle BRT. Pour les jeux de données considérés comme faibles (n<1000), une méthode de validation croisée est utilisée pour ajuster le modèle.

Résultats et Conclusions

Sept prédicteurs expliquent en moyenne entre 13% et 8% de la variabilité des prises (respectivement Year, Dist-A-Eddies, SST, EKE, MLD, Dist-C-Eddies et bathy), les autres entre 4.6% et 1.8%. Le prédicteur season montre une contribution stable (3.6% en moyenne).

Un résultat surprenant est la contribution exceptionnellement basse de la chlorophylle a (3.9%) bien que des captures exceptionnelles de YFT aient été reportées en association avec des blooms exceptionnels dans l'Océan Indien (Fonteneau et al. 2006).

De même, et malgré des efforts importants pour quantifier objectivement la dynamique temporelle de la chlorophylle a, (SSC_dynm), sa contribution à la variabilité des prises reste extrêmement basse (1.8%), au plus bas de toutes les variables testées, soit encore trois fois moins que la SSC. Cela signifie que les conséquences des enrichissements locaux sub-superficiels ne sont pas significativement "attractifs" pour les thons, probablement à cause des décalages à la fois spatiaux et temporels qui existent vraisemblablement entre les enrichissements primaires et la présence significative de proies entre un et deux niveaux trophiques au delà.

De même, et malgré le nombre important d'associations qualitatives de thonidés avec des structures frontales décrites dans la littérature, l'importance des fronts, même séparés en terme d'intensité, n'est pas aussi importante que pressentie (3.3% et 3.4%). Le fait que ces structures soient mouvantes et associées à des processus physiques distincts (courants côtiers, tourbillons, etc.) fait que leur importance est statistiquement secondaire en rapport avec les effets des tourbillons, pour lesquels des indicateurs spécifiques ont été développés.

Il est également probable que des études régionales montreraient des relations plus précises, en particulier dans le Pacifique Est où des systèmes très contrastés entraînent une difficulté d'ajustement de ces variables au sein des modèles à cause de leur effet sans doute très différents sur les prises. C'est aussi sans doute le cas dans l'Océan Indien entre l'upwelling côtier de Somalie et la région équatoriale chaude où des processus fondamentalement très différents se produisent.

A l'opposé, les tourbillons sont des structures pérennes (semaines ou mois) où virtuellement toute une chaîne trophique peut se développer, avec une relation plus forte et durable en terme d'intérêt trophique qu'à travers des fronts thermiques ou des blooms planctoniques.

Chapter 1 1 Introduction

Temperature, wind speed, salinity, pigment concentration are some of the variables frequently used to characterize the marine environment. Variability in these parameters is largely derived from physical oceanographic processes that take place at different temporal and spatial scales. Consequently, when studying interactions between an organism or group of organisms and their environment(s), it is important to consider these processes and their effects. For example, biogeochemical characteristics at a regional scale can be influenced by mesoscale features (e.g., eddies and fronts) that act to either enhance or reduce local productivity, and consequently, the availability of prey. Thus, these mesoscale dynamics can influence variables such as the growth and development of marine species. Understanding these bio-physical interactions is the key to characterize a habitat and to identify, the individual environmental conditions that determine the distribution of a given species. To date, studies have been conducted to describe the environmental preferences of a variety of marine species, including birds [START_REF] Buckley | Habitat Selection and Marine Birds[END_REF][START_REF] Tremblay | A review of analytical approaches to investigate seabird-environment interactions[END_REF][START_REF] Kappes | Hawaiian albatrosses track interannual variability of marine habitats in the North Pacific[END_REF]), mammals (MacLeod et al. 2007;[START_REF] Block | Revealing pelagic habitat use: the tagging of Pacific pelagics program[END_REF], sharks [START_REF] Stokesbury | Movement and environmental preferences of Greenland sharks (Somniosus microcephalus) electronically tagged in the St. Lawrence Estuary, Canada[END_REF] and turtles [START_REF] Block | Revealing pelagic habitat use: the tagging of Pacific pelagics program[END_REF]. In other studies, the focus has been on identifying biological hotspots in the marine environment, biogeographic regions recognized by their high biodiversity [START_REF] Palacios | Oceanographic characteristics of biological hot spots in the North Pacific: a remote sensing perspective[END_REF].

For large pelagic fishes, such as tropical tunas, four environmental variables are most commonly used to characterize their habitat: sea surface temperature (SST), sea surface chlorophyll (SSC), sea level height (SSH), and mixed layer depth (MLD). Some studies also consider mesoscale features, such as thermal fronts, because of their marked impact on the tuna distributions. In addition to describing tuna habitat, some authors have also attempted to show how tuna species will respond to changes in their habitat. For example, yellowfin tunas, Thunnus albacares (Bonnaterre, 1788), have been shown to change their foraging strategy in the face of environmental change, extending their foraging range into regions that fall beyond their normal SST preferences [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF]).

In the study of tropical tunas distribution, SST is one of the most investigated environmental variables [START_REF] Andrade | Skipjack tuna fishery in relation to sea surface temperature off the southern Brazilian coast[END_REF][START_REF] Barkley | Skipjack tuna, Katsuwonus pelamis, habitat based on temperature and oxygen requirements[END_REF][START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF][START_REF] Stretta | Forecasting models for tuna fishery with aerospatial remote sensing[END_REF][START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF]. For example, [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] examined Sri Lankan longline fishery data (northeast Indian Ocean) and found that high levels of yellowfin tuna catches occurred in areas where SST ranged from 28°C to 30°C. In another study, [START_REF] Hanamoto | Effect of oceanographic environment on Bigeye tuna distribution[END_REF] noted that bigeye tuna have a distinctive depth distribution (between 0 m-600 m), different from other tuna species, and an optimum SST range that varies between 10°C-15°C and 10°C-18°C. SST is also a key parameter in statistical models used to predict the presence of skipjack tuna (Katsuwonus pelamis (Linnaeus, 1758)) in scenarios where different fishing gears are being used [START_REF] Barkley | Skipjack tuna, Katsuwonus pelamis, habitat based on temperature and oxygen requirements[END_REF][START_REF] Evans | Propagation of thermal fronts in the somali current system[END_REF]Andrade & Garcia 1999a;Dueri et al. 2012) used SST as an indicator to determine tolerance levels in skipjack tuna when faced with significant environmental changes. Meanwhile, [START_REF] Evans | Propagation of thermal fronts in the somali current system[END_REF] and [START_REF] Stretta | Forecasting models for tuna fishery with aerospatial remote sensing[END_REF] determined that the distribution of yellowfin tuna is affected by SST, with this species preferring warmer waters. Their distribution is also influenced by sub-surface water temperature (Bautista-Cortés 1997) and the depth of the thermocline [START_REF] Schaefer | Simultaneous behavior of skipjack (Katsuwonus pelamis), bigeye (Thunnus obsesus), and yellowfin (T. albacares) tunas, within large multi-species aggregations associated with drifting fish aggregating devices (FADs) in the equatorial eastern Pacific Ocean[END_REF]. Indeed, while SST is one of the key environmental variables to consider, there are a range of other variables that are also important. For example, salinity is known to have a relevant influence on the distribution of tuna in general, but for bigeye tuna it is one of the main variables used in distribution studies (Song & Gao, 2006). Another example is wind speed [START_REF] Pereira | Behaviour of bigeye tuna in a baitboat fishery[END_REF] observed a strong relationship between wind speeds of 0.5 m/s -5.4 m/s (1 to 3 on the Beaufort scale) and the number of fishing events and their respective catches of bigeye tuna. This study also noted that when there was no wind, the values of both fishing events and catch were low. It is also interesting to point that this author found that the majority of catches were made in areas with an SST ranging between 16°C-20°C.

Additional important parameters are the sea surface chlorophyll (SSC or Chl-a) and the net primary production. [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] found that there was a strong association between Chl-a concentration (values ranging between 0.1 mg.m -3 -0.4 mg.m -3 ) and yellowfin tuna catches. Overall, existing research suggests that high concentrations of Chl-a are needed to produce significant catches; however, the exact dynamics of this relationship can vary from one ecosystem to another as tuna adapt their foraging and survival strategies.

Mesoscale features can also play an important role in influencing the distribution of tropical tunas, and consequently, the structuration of their fishing grounds (Figure 1). The present study strongly emphasizes the detection and classification of thermal fronts, and it is motivated by the lack of information and quantification about the association of tunas with a specific type of front (i.e. different thermal gradients). The objective is to identify the tuna preference for a given temperature gradient intensity. Since the 1960s, considerable information about this association has been gathered from numerous locations including the North and Central Pacific Ocean [START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF]Zainuddin et al. 2004a;[START_REF] Zainuddin | Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data[END_REF], the coast of California [START_REF] Fiedler | Tuna aggregation and feeding near fronts observed in satellite imagery[END_REF][START_REF] Laurs | Albacore tuna catch distributions relative to environmental features observed from satellites[END_REF], and the South Atlantic Ocean [START_REF] Andrade | The relationship between the skipjack tuna (Katsuwonus pelamis) fishery and seasonal temperature variability in the south-western Atlantic[END_REF][START_REF] Zagaglia | Remote sensing data and longline catches of yellowfin tuna (Thunnus albacares) in the equatorial Atlantic[END_REF]) to support the strong link between tuna distribution and the presence of thermal fronts. Further studies also highlight the importance of fronts, for example, [START_REF] Sagarminaga | Spatio-temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment[END_REF][START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF] found that albacore tuna displays an association with ocean fronts because of their strong links with horizontal gradients in temperature, salinity and SSC. Defined as the boundary between two water masses of different characteristics, frontal features are ubiquitous structures that mainly occur close to the ocean surface and at spatial scales ranging from 1 km-1000 km [START_REF] Fedorov | The physical nature and structure of oceanic fronts[END_REF]. Activity in these frontal areas can be very dynamic, often acting to transfer significant amounts of energy from large to small scales [START_REF] Ullman | Satellite-derived sea surface temperature fronts on the continental shelf off the northeast U.S. coast[END_REF]. When a front develops (through a process referred to as frontogenesis), a convergence zone between the two differing water masses is created. Nutrients at the surface gather in this zone and, depending on the persistence of the front, this process can lead to enhanced primary production. In turn, these zones of enhanced primary production can act to aggregate marine organisms from across all trophic levels [START_REF] Bakun | Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage[END_REF]. Indeed, it is this process that explains, at least in part, the association between these features and tuna distributions [START_REF] Blackburn | Distribution of forage of skipjack tuna (Euthynnus pelamis) in the Eastern Tropical Pacific[END_REF]Dufour, P. & Stretta, J.M 1973;Maul, G.A. et al. 1984;[START_REF] Podestá | Exploring the association between swordfish catch rates and thermal fronts on U.S. longline grounds in the western North Atlantic[END_REF]. Other explanatory factors may include the confinement of tuna to a physiologically optimal temperature range [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF]; their use of frontal gradients to determine migration routes [START_REF] Humston | Schooling and migration of large pelagic fishes relative to environmental cues[END_REF]Maul, G.A. et al. 1984) and to thermoregulate [START_REF] Neill | Magnitude and ecological implications of thermal inertia in skipjack tuna,Katsuwonus pelamis (Linnaeus)[END_REF]).

The frequency of bluefin tuna (Thunnus thynnus) fishing sets in the Gulf of Mexico was found to be related to both local SST and surface thermal fronts marked by strong temperature gradients. However, when the researchers focused specifically on catch per unit effort, they found that it was only related to thermal fronts [START_REF] Maul | Remotey sensed oceanographic patterns and variability of bluefin tuna catch in the Gulf of Mexico[END_REF]. More recently, (Nieto et al. 2015a) described the association between albacore tuna in the North Pacific and the persistent upwelling front off the coast of California. However, as in the case of albacore tuna, this time in the North Atlantic, the majority of studies that show such relationship are based only on visual interpretations of catch locations and the presence of surface fronts. The type of gradient intensity that define those fronts is seldom investigated or characterized. The types of fronts described are usually those derived from upwelling processes, eddies interactions and river plumes observed on specific dates. This highlights the current need to identify frontal structures using quantitative statistical approaches, for example, automatic front detection algorithms and characterization techniques.

Mesoscale eddies are another ubiquitous features in the ocean. Unlike fronts, eddies are not present in the tropics due the absence of the Coriolis effect. However, they do appear beyond 5°N and 5°S. Eddies play an important role in the global ocean heat budget and general circulation of the ocean [START_REF] Siegel | Bio-optical footprints created by mesoscale eddies in the Sargasso Sea[END_REF]. They have been linked to the vertical transport of nutrients and consequently, are known to be able to both enhance or reduce productivity at the surface and sub-surface of the ocean. Eddies occur when two major forces are balanced: the horizontal pressure gradient force (which arises from differences in water density) and the Coriolis force. Eddies are commonly generated from frontal instabilities along which cold and warm water masses twist together in a circular fashion [START_REF] Mann | Dynamics of Marine Ecosystems: Biological-Physical Interactions in the Oceans[END_REF] (Figure 2). Eddies can also form as seawater flows over seamounts or due to wind forcing in the lee of the islands (e.g., the Hawaiian Islands or the Canary Islands; [START_REF] Seki | Biological enhancement at cyclonic eddies tracked with GOES thermal imagery in Hawaiian waters[END_REF]). Long, westward-travelling, planetary-scale Rossby waves are also likely to play a role in eddy formation at a global scale.

Depending on their flow direction, eddies can be classified as either cyclonic or anticyclonic. A forced anticyclonic eddy presents a convergent surface flow pattern at its center, thus promoting a warmer, nutrientpoor eddy core. This flow pattern tends to favor the development of convergent frontal structures around the interior of the eddy. Conversely, a forced cyclonic eddy presents a divergent surface flow pattern at its center, thus promoting a cooler, nutrient-rich eddy core. This latter eddy type is more likely to aggregate planktonic organisms [START_REF] Bakun | Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage[END_REF][START_REF] Olson | modeling pelagic biogeography[END_REF]. A third type of eddy, namely the mode-water eddy, was described by [START_REF] Mcgillicuddy | Eddy/Wind Interactions Stimulate Extraordinary Mid-Ocean Plankton Blooms[END_REF]) based on offshore in situ observations made in the North Atlantic Ocean. This eddy behaves similarly to an anticyclonic eddy; however, it has a cold water core that is characterized by high concentrations of Chl-a. This last type of eddy is considered in the present study as "anticyclonic" because its surface characteristics are similar (positive anomaly of the sea surface height). The Coriolis effect dictates which direction eddies rotate. In the northern hemisphere, anticyclonic eddies rotate clockwise while cyclonic eddies rotate counter-clockwise while the opposite behavior is observed in the southern hemisphere. Eddies and fronts can be found singly or grouped, both by type or mixed, in nearly every seawater body on this planet (including Arctic regions and off the coast of Antarctica) and at all depths. However, the ecological effects of these features are more evident near the surface [START_REF] Owen | Fronts and Eddies in the Sea: Mechanisms, Interactions and Biological Effects[END_REF].

A reason why eddy features are considered important is due to their influence on primary production via enrichment processes. The key factors that dictate this influence can be summarized as their effect on biogeochemical processes through their contributions to vertical nutrient transport and their ability to alter the underwater light field [START_REF] Falkowski | Role of eddy pumping in enhancing primary production in the ocean[END_REF][START_REF] Klein | The Oceanic Vertical Pump Induced by Mesoscale and Submesoscale Turbulence[END_REF][START_REF] Siegel | Bio-optical footprints created by mesoscale eddies in the Sargasso Sea[END_REF]). These effects are: 1) their ability to transport water parcels as they move [START_REF] Chelton | Global observations of nonlinear mesoscale eddies[END_REF][START_REF] Mcgillicuddy | Eddy/Wind Interactions Stimulate Extraordinary Mid-Ocean Plankton Blooms[END_REF][START_REF] Siegel | Bio-optical footprints created by mesoscale eddies in the Sargasso Sea[END_REF], 2) the intermittent upwelling (eddy pumping) 3) the exchange of water across the frontal boundary, thereby contributing to a significant flux of nutrients and enhanced biological productivity [START_REF] Falkowski | Role of eddy pumping in enhancing primary production in the ocean[END_REF][START_REF] Garrett | Dynamical Aspects of Shallow Sea Fronts[END_REF][START_REF] Mann | Dynamics of Marine Ecosystems: Biological-Physical Interactions in the Oceans[END_REF].

The relationship between eddies and tuna distribution has been already shown in previous studies. For example, the highly productive fishing grounds of albacore tuna in the North Pacific is characterized by the presence of specific mesoscale features around the Kuroshio and Oyashio currents [START_REF] Zainuddin | Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data[END_REF]). In the Mozambique Channel, Tew [START_REF] Tew Kai | Influence of mesoscale eddies on spatial structuring of top predators' communities in the Mozambique Channel[END_REF] identified an association between free swimming tuna schools and the core and edge regions of mesoscale eddies by comparing position data of tuna catches with sea level anomaly and SSC maps. Furthermore, bio-physical variations in the world's oceans also play an important role in the abundance and distribution (therefore in catchability) of tuna. Changes in climatic conditions, such as El Niño events in the Pacific Ocean or the Dipole in the Indian Ocean, can affect tuna populations. For example, decreases in yellowfin tuna catches in the Indian Ocean in 1984 were linked to a deepening of the thermocline as a result of increased SST [START_REF] Die | Time trends in abundance and catchability of yellowfin tuna and their relationship to the north atlantic oscillation index[END_REF]. This event also affected recruitment in the yellowfin tuna stock and their catchability in the associated purse-seine fishery [START_REF] Lan | Relationship between albacore (Thunnus alalunga) fishing grounds in the Indian Ocean and the thermal environment revealed by cloud-free microwave sea surface temperature[END_REF].

In this study, we will present a detailed description and analysis of the interactions between tuna catches of the purse seine fisheries in the Indian Ocean and the Eastern Pacific Oceans (on free schools, logs, and fish aggregating devices (FADs)) and their environment using a range of general and mesoscale environmental descriptors. Through this work, we aim to achieve three specific objectives:

1. to describe and quantify the frontal structuration of the environment and identify the type of fronts to which tuna can be associated with.

2. to describe the tropical tuna habitat(s) through the analysis of general variables (SST, SSC, SSC dynamics, MLD, Dissolved oxygen), mesoscale variables (EKE, Distance to fronts and Distances to eddy borders) and temporal variables as (season and year).

3.

To compare the study areas in terms of tuna responses to the different environmental conditions.

In order to achieve these goals, we developed this study in the following chapters: Chapter 2 describes the main oceanographic characteristics in both study areas, the Indian Ocean and the Eastern Pacific Ocean. Chapter 3 provides a general description of the tropical tuna species found in the two study areas, the relevant purse-seine fisheries and their associated fishing modes. Chapter 4 presents the fisheries data and the environmental data used in the analyses. Chapter 5 -outlines the specific techniques developed to detect thermal fronts at two different scales (basin and 4 km resolution); and classify them based on their intensity gradient. Chapters 6 and 7 present the multivariate analysis we undertook to identify which environmental preferences determined the distributions and catch level of the three tuna species considered and a brief description of the environmental conditions of the Indian and Pacific oceans. Chapter 8 present the general conclusions and perspectives. This chapter presents the principal oceanographic characteristics of the Indian Ocean (IO) and the Eastern Pacific Ocean (EPO), starting with a comparative approach based in the specific oceanographic characteristics of each basin. Then each ocean is described trough some of the most important environmental variables as the sea surface temperature (SST), the sea surface chlorophyll (SSC), the dissolved oxygen, the mixed layer depth (MLD) and the eddy kinetic energy (EKE). Because of their role in the origin of the mesoscale structures (e.g. eddies and fronts) the current patterns are also described along with the variables already mentioned.

Large scale comparison between Indian Ocean and Eastern Pacific Ocean.

• The IO is bounded to the north by the Asian continent, preventing northward heat export and only allowing weak ventilation of the IO thermocline from the north.

• The Asian continent drives the strongest monsoon on earth, and the monsoonal winds generate large seasonal variations in ocean currents, many of which display annual reversals such as the Somali current and the Southwest/Northeast Monsoon Current south of India and SriLanka.

• There is no equatorial upwelling in the eastern Indian Ocean in contrast with the Atlantic and Pacific oceans.

• Upwelling occurs in the northern hemisphere off northwest Africa and the Arabian Peninsula and east and west of the tip of India and in the southern hemisphere along the northern edge of the southeast trades.

• The Somalian upwelling is supplied by a shallow cross-equatorial cell (CEC) that again does not exist in the other oceans and carries most of the cross-equatorial heat transport.

• The IO has a low-latitude exchange route with the Pacific, the Indonesian Throughflow.

• The presence of the Inter-topical convergence zone always the northern hemisphere and its seasonal oscillation between 5°N (winter) and 10°N (summer), define in an important way the oceanography of the EPO.

• Presence of the Pacific warm pool an open-ocean biogeographic province with a well-defined biological community.

• The most important characteristic that contribute to the uniqueness of this basin is the shallow and strong thermocline in the entire EPO.

• Presence of the narrow wind jets, in the EPO, blowing from land to sea: The Tehuantepec, the Papagayo and the Panama Jets. Although they are no present all year round, their effect is detectable in mean distributions of Chla and SST.

Indian Ocean

The oceanographic characteristics of the Indian Ocean (IO) strongly differ from the Atlantic and Pacific basins, mainly because the IO is considered as a "closed ocean" having been bounded in the north by the Asian continent. The thermal contrast between land and sea creates a seasonal wind reversal and a deep seasonal variability in their currents [START_REF] Fieux | Current system in the Indian ocean[END_REF], especially affecting the Arabian Sea and the Bay of Bengal, located in the north of the basin on each side of the Indian Peninsula [START_REF] Fieux | Current system in the Indian ocean[END_REF]. The specific current patterns generate an important frontal and eddy activity making the IO an ideal ocean to investigate its mesoscale dynamic [START_REF] Roa-Pascuali | Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application[END_REF]. The western boundary in the south of the basin is modified by the presence of Madagascar Island and the Mozambique Chanel, while in the east of the basin, the IO is connected with the equatorial Pacific Ocean through the deep passage of the Indonesian seas.

The tropical Indian Ocean is highly influenced by strong winds that characterize two important seasons with a reversal behavior known as the monsoon system. This characteristic make the IO one of the largest warm pool on earth, after the pool in the Pacific Ocean (regions with SST >28°C). The peculiarity of the pool in the IO is its seasonal variation [START_REF] Vinayachandran | Phytoplankton bloom in the Bay of Bengal during the northeast monsoon and its intensification by cyclones[END_REF]. The IO exhibits a number of modes of climate variability, ranging from intraseasonal to interannual and longer time scales, most of which are coupled to the strong seasonal cycle [START_REF] Schott | Indian Ocean circulation and climate variability[END_REF]). There are four climatic seasons in this ocean, two strong seasons related to the inversion of the Monsoon currents known as the North-eastern Monsoon (NE-M) from December to March and the South-Western Monsoon (SW-M) from June-September. Two other seasons, of moderate intensity, are the inter-monsoons from April to May and from October to November (Fig. 2.3).

Eastern Pacific Ocean

The Eastern Pacific is an "open ocean" without continent blocking the water circulation to the north or south of the basin, as happens in the IO. Because of the high spatial variability of the ocean-atmospheric forcing agents [START_REF] Amador | Atmospheric forcing of the eastern tropical Pacific: A review[END_REF] the EPO is a unique and diverse environment to study bio-physical processes and their impacts in the trophic chain [START_REF] Lavín | A review of eastern tropical Pacific oceanography: Summary[END_REF]) (Fig. 2.4).

The role of the Ekman transport and the effect of the vertical mixing in the region are well observed through the distribution of some parameters such as the sea surface temperature, the sea surface salinity, the depth of the thermocline and the mixed layer depth [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF]. At the same time mesoscale features as fronts, eddies and meanders are generated by Ekman pumping [START_REF] Willett | Eddies and Tropical Instability Waves in the eastern tropical Pacific: A review[END_REF] during the coastal and open ocean upwellings. Additionally in the EPO the topography of the thermocline is related to the effect of the geostrophic balance and Rossby and Kelvin waves [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF]. The atmosphere of the EPO boasts three examples of a rare condition, narrow wind jets blowing from land to sea: the Tehuantepec, the Papagayo and the Panama Jets (Fig. 2.4). Although they are intermittent and occur only from November to April, their effect is well pronounced in the mean distributions of SST and the SSC.

Oceanographic characteristics

Sea Surface temperature (SST) Indian Ocean

The Indian Ocean warm pool (IOWP) can cover the equatorial region east of about of 50°E, the Bay of Bengal and the eastern Arabian Sea. The importance of this pool for atmospheric processes over the Indian Ocean has been recognized. [START_REF] Joseph | Interannual Variability of the Onset of the Indian Summer Monsoon and Its Association with Atmospheric Features, El Niño, and Sea Surface Temperature Anomalies[END_REF] found that onset vortices of the Indian summer monsoon form over the warmest regions of the Indian Ocean. A good part of the IOWP has outgoing longwave radiation below 240 Wm -2 indicating that it is a region of deep convection. IOPW is expected to play a role in the development of cyclones over the Bay of Bengal, some of which have been among the most destructive [START_REF] Vinayachandran | The warm pool in the Indian Ocean[END_REF].

The warming phase of the IOWP begins in February, when the pool begins to spread on both sides of the equator (see Appendix A). This continues through April. The northern boundary of the pool merges with the coastline during April in the Bay of Bengal and in May in the Arabian Sea. The first signal of cooling are observed in the southern IO during May. The summer monsoonal cooling begins during June in the western equatorial IO and during July in the Arabian Sea and continues trough September. In the Bay of Bengal, the SST does not fall bellow 28°C during summer. After the withdrawal of the monsoon a secondary warming takes place in the Arabian Sea during October. This is followed by winter cooling of the Indian Ocean north of the equator and summer warming in the southern IO. The warm pool in the Bay of Bengal recedes during October-November (see Appendix A). The maximum depth of the 28°C isotherm is 100 m, shallower than the western Pacific Ocean, during March, May, June and October [START_REF] Vinayachandran | The warm pool in the Indian Ocean[END_REF].

Therefore monsoon cyclonic storms are important to drain the accumulate heat energy through intense airsea interaction and energetic internal oceanic redistribution processes to accomplish thermal equilibrium over an annual cycle, highlighting the importance of the Arabian Sea surface temperatures (SST) on the prediction of summer monsoonal onset over India [START_REF] Rao | Seasonal variability of near-surface thermal structure and heat budget of the mixed layer of the tropical Indian Ocean from a new global ocean temperature climatology[END_REF] A unique wind forcing pattern occurs over the Indian Ocean. It involves the occurrence of semi-anual eastward winds over the equator, during April to June and October to November (Fig. 2.5). These winds generate an eastward annual-mean equatorial zonal wind stress that increase surface temperatures along the equator, a strong difference with other tropical oceans [START_REF] Rao | Seasonal variability of near-surface thermal structure and heat budget of the mixed layer of the tropical Indian Ocean from a new global ocean temperature climatology[END_REF].

Eastern Pacific Ocean

The sea surface wind in the Eastern Tropical Pacific is determined by the trade winds and their area of convergence, the Inter-tropical convergence zone (ITCZ). The ITCZ creates a weaker wind area to the west of Mexico and Central America where the evaporation and the vertical mixing are low in the presence of strong surface heat input, the thermocline is shallow and the sea surface temperature is higher than elsewhere in the region [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF]. This region is known as the Eastern Pacific Warm Pool (Fig. 2.6). [START_REF] Ballance | Oceanographic influences on seabirds and cetaceans of the eastern tropical Pacific: A review[END_REF] describe a clear association between specific seabirds, yellowfin tuna and some dolphin species as a special characteristic of this warm pool.

The warm pool is bounded on the south by a sharp SST gradient north of the equator that separates the equatorial cold tongue from the warm water to the north. Deep atmospheric convection with heavy precipitations is generally confined to the north of this equatorial front, with dry conditions prevailing on and south of the equator. This region of strong atmospheric convection convergence is called the Inter Tropical Convergence Zone (ITCZ). This climatic asymmetry between the north and south of the equator results from air-sea interaction triggered by latitudinal asymmetries in the continental geometry. The eastern Pacific warm pool is climatically important, supporting one of the major convection centers of the global atmosphere.

The equatorial cold water tongue, that define the upwelling in the Equatorial Pacific Ocean, stretches from the coast of Peru to the date line (or 180 th meridian of longitude) [START_REF] Rafferty | Oceans and Oceanography[END_REF]). The tongue is partly caused by horizontal advection of cool water from east and partly by the upwelling. This upwelling is caused by the divergence of the Ekman flow at the equator which is partly opposed by the convergence of geostrophic flow due to the general eat-west slope of the sea level along the equator [START_REF] Wyrtki | An Estimate of Equatorial Upwelling in the Pacific[END_REF]. There is a close connection between the tongue and the cold upwelling water along Peru. To the north of the tongue there is an association of the low salinity surface water with the North Equatorial Counter-current. During the year a sea surface temperature of 26°C extends along 8°N from the western to the eastern Pacific coinciding with the counter-current, to the south a ridge of maximum temperatures extends eastward along 8°S from western Pacific to 90°W and beyond (Wyrtki, 1964[START_REF] Wyrtki | An Estimate of Equatorial Upwelling in the Pacific[END_REF]) (Fig. 2

.6).

The equatorial cold tongue is best developed during the southern winter when the southeast trade winds are stronger (August to October). The temperatures south of Galapagos Islands are ~20°C and the tongue is connected with the upwelling off Peru with a minimum temperature of 2°C. The differences in temperature between the east (Galapagos Islands) and the west of the tongue (180°W longitude) is 8°C. A well-defined front is present near 3°N and forms the northern limit of the tongue [START_REF] Wyrtki | An Estimate of Equatorial Upwelling in the Pacific[END_REF]. Here, in the lower half of the isothermally mixed layer the geostrophic equatorward flow enhanced mixed layer convergence.

Cold water is upwelled on or south of the equator and is advected poleward by the mean mixed layer flow and by eddies. Cold advection is confined by the mixed layer current convergence in the north, keepingthe SST fronts close to the equator. Warm advection from the north and cold from the south strengthen the front. In the Southern hemisphere, a continuous southwestward current advects cold water far from the upwelling core [START_REF] De Szoeke | What Maintains the SST Front North of the Eastern Pacific Equatorial Cold Tongue?*[END_REF]). The opposite condition of the tongue is observed during the southern summer (February-March) when the southeast trade winds are weakest over the eastern half of the tongue. The connection with upwelling off Peru is less direct, the warm waters north and south of Galapagos Islands are connected by higher temperatures ~26°C. Between 110 and 120 °W the upwelling is observed with temperatures lower than 25°C [START_REF] Wyrtki | An Estimate of Equatorial Upwelling in the Pacific[END_REF].

Sea surface chlorophyll (SSC) Indian Ocean

The main biological enrichments are due to coastal upwelling, one off Somalia and another off the Arabian Sea, in the coast of Oman (Fig. 2.5). There is also a weaker upwelling during the SW monsoon off the east and west coast of India. The west Indian upwelling is weak, with SST of 26°C only ~2°C colder than further offshore [START_REF] Schott | The shallow overturning circulation of the Indian Ocean[END_REF].In the southern hemisphere, during July-September when the Java current is north-westward there is a convergence with the southeastward Sumatra current south of Sumatra. During this season an upwelling occurs along the Java coast. This seasonal upwelling is the only major upwelling in a western boundary of an ocean [START_REF] Mann | Dynamics of Marine Ecosystems: Biological-Physical Interactions in the Oceans[END_REF]. It occurs during the southwest monsoon season (June-September) derived from the Arabian Sea boundary current known as Somali Current. This current flows southward along the coast of Somalia during December-February and flows northward during March-September [START_REF] Shenoi | Near-surface circulation and kinetic energy in the tropical Indian Ocean derived from Lagrangian drifters[END_REF], it follows the coast of Somalia and Oman and produces the coastal upwelling. At that moment of a bloom derived from the upwelling, the phytoplankton is composed mainly by diatoms. This diatoms are grazed by the mesozooplankton, themselves consumed by fishes. Much of the phytoplankton sinks below the mixed layer and decomposes, giving rise to a large carbon flux. During the inter-monsoon periods the waters become oligotrophic with a low primary production (0.7 g C m -2 d -1 ).

The upwelling in the coast of Oman occurs during the summer monsoon when the offshore flow is carried by filaments far into the sea. The filaments develop in conjunction with mesoscale nearshore features and have a life time of a few weeks (Lee, Jones, Brink & Fischer, 2000[START_REF] Schott | The shallow overturning circulation of the Indian Ocean[END_REF].

Eastern Pacific Ocean

The equatorial upwelling makes available sub-thermoclinal nutrient-rich waters, while iron an limitation results from the high nitrate-low chlorophyll conditions. Because of this iron-limitation an important microbial loop is developed with a warming of upwelled water [START_REF] Pennington | Primary production in the eastern tropical Pacific: A review[END_REF]. At 10°N below the ITCZ, a lif of the thermocline is caused by Ekman transport divergence across the ITCZ. It gives rise to the North Equatorial Counter-current (NECC) with an associated band of high nitrate concentration and increased production [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF][START_REF] Pennington | Primary production in the eastern tropical Pacific: A review[END_REF]). The warm pool described before is divided by this 10°N thermocline ridge which is an important physical feature for many apex predators [START_REF] Ballance | Oceanographic influences on seabirds and cetaceans of the eastern tropical Pacific: A review[END_REF].

A specific open ocean upwelling with great biological importance is the Costa Rica Dome. Similar to others tropical thermocline domes, this dome is unique because it is also forced by a coastal wind jet [START_REF] Fiedler | The annual cycle and biological effects of the Costa Rica Dome[END_REF]. Located west of Central America it is described by [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF] as the result of the seasonal combination of coastal jets, eddies, the ITCZ and a geographic balance at the eastern extreme of the 10°N thermocline ridge. This ridge and the dome extend below the thermocline to a depth of more than 300 m (Fig. 2.7).

The coastal jets derived of the influence of the Central American topography on the ocean-atmosphere coupled system play an important role in the biophysical properties of the EPO [START_REF] Rodriguez-Rubio | Variability of photosynthetic pigments in the Colombian Pacific Ocean and its relationship with the wind field using ADEOS-I data[END_REF][START_REF] Xie | Air-Sea Interaction over the Eastern Pacific Warm Pool: Gap Winds, Thermocline Dome, and Atmospheric Convection*[END_REF]. The presence of the Sierra Madre, the Central American cordillera which reaches heights of around 1000m contains three elevation gaps (~300m) located near the gulfs of Tehuantepec (southwest Mexico, 20°N), Papagayo (Nicaragua-Costa Rica boundary 10°N) and of the Panama Bight (Panama Pacific coast). They allow intense wind-jets to blow from the Caribbean to tropical Pacific regions [START_REF] Chaigneau | Lagrangian study of the Panama Bight and surrounding regions[END_REF]) [START_REF] Fiedler | The annual cycle and biological effects of the Costa Rica Dome[END_REF] explains the annual variability of the dome in four steps: 1) coastal shoaling of the thermocline off the Gulf of Papagayo during February-April, forced by Ekman pumping on the equatorward side of the Papagayo wind Jet (Fig. 2.4); 2) separation from the coast during May-June when the ITCZ moves north to the counter-current thermocline ridge; 3) counter-current thermocline ridging during July-November, when the dome expands to the west as the counter-current thermocline ridge shoals beneath a band of cyclonic wind stress curl on the poleward side of the ITCZ and 4) deepening during December-January when the ITCZ move south and strong trade winds blow over the dome. Coastal eddies may be involved in the coastal shoaling observed during February-March. Cyclonic and Anticyclonic eddies have been observed propagating from the coast near the Gulf of Papagayo. These last ones are generated by winter winds jets at the Gulfs of Tehuantepec and Papagayo (Willet, 1996;[START_REF] Fiedler | The annual cycle and biological effects of the Costa Rica Dome[END_REF]. On the other hand, regarding very productive upwelling areas in the EPO region, the coastal upwelling off Peru -caused by the predominant alongshore equatoward winds-is the most important and productive upwelling system in the Eastern tropical Pacific Ocean. The Humboldt current system is notable for its high production of anchoveta, the largest monospecific fishery in the world. This system extends from southern Chile (~45°S) to northern Peru (~4°S) where upwelled waters interract with warm tropical waters form the equatorial front [START_REF] Chavez | A comparison of Eastern Boundary Upwelling Ecosystems[END_REF] These authors state that the Humboldt Current system has the weakest winds compared to other eastern boundary upwelling systems, as the California and Canary ones, and the greatest average upwelled volume. This system also has the shallowest, more oxygen depleted and the more extended Oxygen Minimum Zone (Fig. 2.9).

Mixed Layer Depth (MLD)

Characterized by the homogeneous temperature, salinity and density, the upper layer in the oceans is the result of the active vertical mixing near the surface, promoted by processes like wind-stirring, waves, turbulence generation by vertical shear or night time convective mixing. The mixed layer is very important in establishing the world's ocean mean state and variability, as it acts as an interface between the atmosphere and the ocean interior [START_REF] Keerthi | Interannual variability of the Tropical Indian Ocean mixed layer depth[END_REF]

Indian Ocean

One of the most important characteristic of the Arabian Sea is the deepening and cooling of the mixed layer in the central northwestern areas during both the North-Eastern and South-Western Monsoon seasons [START_REF] Rao | Evolution of the Climatological Near-Surface Thermal Structure of the Tropical Indian Ocean 1. Description of Mean Monthly Mixed Layer Depth, and Sea Surface Temperature, Surface Current, and Surface Meteorological Fields[END_REF]) An explanation for the deepening and observed gradients during the SW Monsoon is given by [START_REF] Bauer | Influence of monsoonally-forced Ekman dynamics upon surface layer depth and plankton biomass distribution in the Arabian Sea. Deep Sea Research Part A[END_REF]who found that the shoreward displacement of the Findlater Jet open-ocean upwelling pulls the thermocline closer to the surface, yielding shallower mixed layers, while on the offshore side wind-stress-curl-driven downwelling deepens the mixed layer. More recently [START_REF] Lee | The upper-ocean response to monsoonal forcing in the Arabian Sea: seasonal and spatial variability[END_REF] have shown that variations in turbulent wind driven entrainment are more likely the cause of this gradient in MLD, though this cannot fully explain the observed MLD spatial variability, also strongly influenced by horizontal advection [START_REF] Rao | Evolution of the Climatological Near-Surface Thermal Structure of the Tropical Indian Ocean 1. Description of Mean Monthly Mixed Layer Depth, and Sea Surface Temperature, Surface Current, and Surface Meteorological Fields[END_REF]. Moored observations found that the deepening and cooling of the mixed layer was locally forced during the NE Monsoon primarily by the negative surface heat flux, and during the South-west monsoon by the wind stress [START_REF] Weller | Moored observations of upper-ocean response to the monsoons in the Arabian Sea during 1994-1995[END_REF]. During the Inter-monsoon seasons (April-May and October-November) weaker winds and strong solar irradiation result in a thin and warm MLD.

Compared to the Arabian Sea, the Bay of Bengal exhibits weaker seasonal MLD variations [START_REF] Gopalakrishnan | Study of Radiative and Turbulent Processes in the Stable Boundary Layer under Weak Wind Conditions[END_REF][START_REF] Rao | Evolution of the Climatological Near-Surface Thermal Structure of the Tropical Indian Ocean 1. Description of Mean Monthly Mixed Layer Depth, and Sea Surface Temperature, Surface Current, and Surface Meteorological Fields[END_REF][START_REF] Prasad | A comparison of mixed-layer dynamics between the Arabian Sea and Bay of Bengal: One-dimensional model results[END_REF]). This is especially true in the northern part of the Bay where strong salinity stratification prevents convective cooling. The southern part of the Bay however shows a distinct semi-annual variability with deep MLDs during summer and winter monsoons and shallow MLDs during spring and autumn inter-monsoons, as for the Arabian Sea. Wind-driven mixing largely controls this seasonal variability [START_REF] Narvekar | Seasonal variability of the mixed layer in the central Bay of Bengal and associated changes in nutrients and chlorophyll[END_REF]. At the equator, the Wyrtki Jet that develops during autumn results in a downward sloping of MLD from west to east [START_REF] Ali | Estimation of mixed layer depth in the equatorial Indian Ocean using Geosat altimeter data[END_REF][START_REF] Peffley | A Three-Dimensional Simulation of Coastal Upwelling off Oregon[END_REF], while excess of precipitation in winter results in the formation of a barrier layer west of Sumatra, shoaling the MLD (Masson et al. 2002;Qu and Meyers 2005;Du et al. 2005). The seasonal cycle in the southern tropical Indian Ocean has been less investigated. Seasonal shoaling and deepening of the mixed layer in the south-western Tropical Indian Ocean has been related to annual cycle of the wind, through its stirring effect and its impact on buoyancy fluxes and thermocline depth [START_REF] Foltz | Seasonal Mixed Layer Heat Balance of the Southwestern Tropical Indian Ocean[END_REF]).

In the equatorial Indian Ocean during the Inter-monsoons seasons, as a results of semiannual westerly equatorial winds, a strong eastward surface jets occurs name the Wyrtki Jets (Wjs). These jets are important because they carry warm upper layer waters eastward, increasing the sea level and mixed layer thickness in the east and decreasing them in the west. In the other hand, the surface circulation in the western IO is shallow and strongly reflected in the signature of the thermocline. Warren et al (1966) showed that the topography of the 20°C isotherm is directly proportional to the dynamic topography of the sea surface and closely related to the pattern of near surface flow (Fig. 2.10).

Eastern Pacific Ocean

In the Eastern Tropical Pacific, and to a lesser extent in the eastern tropical Atlantic, the structure of the near surface water column is unusual because the stratification characteristic of tropical oceans is extreme in this region: the permanent thermocline is shallow and strong and associated to a marked halocline [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF]. The temperature gradient of the thermocline usually exceeds 1°C per 10 m, with a large temperature difference between the top and bottom of the thermocline (Fig. 2.10). Its deepening towards the subtropical gyres reflects the geostrophic balance of the large-scale basin circulation [START_REF] Wyrtki | The thermohaline circulation in relation to the general circulation in the oceans[END_REF].

The seasonal cycle of the thermocline depth is of small amplitude (<±10 m) in the far eastern tropical Pacific, between the Galapagos and Costa Rica and along the SW Mexican coast, and along the axis of the counter-current thermocline ridge. Opposite phasing of the seasonal cycle to the north and south of the ridge corresponds approximately to the thermal equator pattern seen in the surface temperature seasonal cycle. This shows that seasonality of net heat flux and wind mixing influences both surface temperature and thermocline depth. Seasonal variability increases to the north and south, except for a slight decrease at the equator, to >± 0 m in the subtropical gyres [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF].

The mixed layer depth (isothermal layer depth) shows spatial and temporal patterns similar to those of thermocline depth. Above the equatorial and counter-current thermocline ridges, and in eastern boundary current waters, the mixed layer is 10-20 m shallower than thermocline. Above the thermocline through between these ridges, the mixed layer is 30-50 m shallower than the thermocline. Poleward and westward of the thermocline ridges, in the subtropical gyres, the mixed layer is 70-100 m deep, but the thermocline is up to 100 m deeper. Differences in patterns of temporal variability of mixed layer and thermocline depths are apparent [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF]. [START_REF] Lukas | The mixed layer of the western equatorial Pacific Ocean[END_REF] have shown the importance of the "barrier layer" beneath the well-mixed surface layer, the result of a strong halocline overlying a deeper thermocline, in the regions of the tropical oceans where the rainfall freshens a surface layer that is mixed to a depth significantly less than the thermocline depth. The barrier layer is <10 m in most Eastern Tropical Pacific, where the shallow thermocline precludes development of a thick barrier layer even where rainfall is high [START_REF] Fiedler | Hydrography of the eastern tropical Pacific: A review[END_REF].

Dissolved Oxygen

The oxygen distribution in the ocean depends on biological processes, like respiration of organisms and physical processes such as currents flow. Changes in these processes should lead to changes in the oxygen distribution. In fact, dissolved oxygen can be viewed as a sensitive early warming system for global climate change in the ocean.

Indian Ocean

Oxygen Minimun Zones (OMZ) correspond to the subsurface oceanic zones (e.g., at 50-100 m depth in the Arabian Sea; [START_REF] Morrison | The oxygen minimum zone in the Arabian Sea during 1995[END_REF]) and reaching ultra low values of O2 concentration (e.g >1 µM; (Karstensen et al. 2008a) OMZ, because of their intensity and shallowness, are, a priori, different from the relatively well known "classical 02 minimum", which is ~50 more oxygenated than OMZ nd found at intermediate depths (1000-1500 m) in all the ocean [START_REF] Wyrtki | The thermohaline circulation in relation to the general circulation in the oceans[END_REF][START_REF] Paulmier | Oxygen minimum zones (OMZs) in the modern ocean[END_REF].

These areas of lowest oxygen in the ocean are referred to as Oxygen Minimum Zones (OMZ). They are vast and can exert a large influence on marine organisms because oxygen is essential for aerobic lige. The largest source of oxygen to the ocean is the atmosphere. Therefore, oxygen concentrations are typically highest at the water-atmosphere interface and decrease with depth. OMZs often occur along upwelling margins where cold, oxygen-depleted water gets transported up from the deep ocean to the surface ocean. However, these waters also brings lots of nutrients to the phytoplankton and fuel production. Therefore as the aerobic bacteria feed on this sinking production just below the ocean's surface they further deplete the oxygen as they respire. The combined effect of oxygen-poor source waters and high oxygen demand by the bacteria can lead to these massive midwater oxygen minima. These minima typically occur between 200 to 1.000 meters below sea surface (Oxygen Minimum Zone Team, 2012).

In the tropics, mid-depth oxygen minimum zones (OMZ) are located in poorly ventilated regions (e.g., Wyrtki, 1962;[START_REF] Kamykowski | Hypoxia in the world ocean as recorded in the historical data set[END_REF]). In the Indian ocean, they are found in the Arabian Sea and the Bay of Bengal, where the ventilation age is 30 years or longer due to their closed northern boundaries. The Arabian sea OMZ (ASOMZ) is the second-most intense OMZ n the world tropical ocean (Kamykowski and Zentara,1990), with near total depletion of oxygen at depths from 200 to 100m (E. G Morrison et al., 1998); indeed, suboxic levels (≤5 µmol O2/Kg) exist over much of that depth range and denitrification occurs in its upper portion [START_REF] Naqvi | Nitrous oxide in the Arabian Sea. Deep Sea Research Part A[END_REF]. Although still strong, the Bay-of-Bengal OMZ (BBOMZ) is weaker than the ASOMZ, with oxygen concentrations everywhere remaining above the denitrification threshold [START_REF] Naqvi | Coastal versus open-ocean denitrification in the Arabian Sea[END_REF]) (Fig. 2.11, 2.12). There is no consensus about processes that could maintain the Indian Ocean OMZs (except that they are both located in poorly ventilated regions). Other hypothesis that have been proposed, related to physical or biological processes.

The seasonal variability of near-surface biological activity is extreme in the Arabian Sea and Bay of Bengal, alternating from high productivity during monsoons to oligotrophic during inter-monsoons periods. In contrast, the seasonal variability of subsurface oxygen is weak (Fig. 2.13). Oxygen budget in the Arabian Sea north of 10° shows a weak annual variability because the variations in oxygen supply (advection of oxygen across 10°N) and demand (remineralization) are seasonally compensated. In contrast, conclude that the annual cycle of the ASOMZ is weak because oxygen supply and demand are both weak, with variations in demand having little impact [START_REF] Mccreary | Dynamics of the Indian-Ocean oxygen minimum zones[END_REF]).

As mentioned above, OMZs are only located in the northern hemisphere. Very low oxygen values indicate a slow renewal rate of thermocline waters in the northern Indian Ocean [START_REF] Tomczak | Regional oceanography: an introduction[END_REF]. In the eastern IO, a weak OMZ is found across the equator, off Indonesia. As in the Pacific, suboxic conditions with oxygen values below 4.5 µmol Kg -1 occur in the Arabian Sea and denitrification has been observed. Indian Central Water and Australasian Mediterranean Water (AAMW) occupy the thermocline of the Indian Ocean. AAMW is a tropical water mas derived from Pacific Ocean Central Water with intense modifications during transit through the Indonesian Sea (Australasian Mediterranean Sea). The rapid inflow of AAMW into the Indian Ocean produces one of the strongest frontal systems of the world ocean's thermocline at about 15°S (Karstensen et al. 2008).

Eastern Pacific Ocean

In the Pacific Ocean, minimum oxygen values are reached at 300-500 m depth which in the EPO became suboxic (dissolved oxygen content <4.5 µmol O2/Kg) with dissolved oxygen concentration of less than 1 µmol O2/Kg. In this ocean, there are two large OMZ regions, one in the North Pacific off Central America and one in the South Pacific off Peru and Chile both reaching far into the central Pacific (Fig. 2.13).

Here the minimum oxygen values may even reach the suboxic level with value below 4.5 µmol Kg -1 (0.1 ml l -1 ) . As the direct ventilation of North Pacific waters is confined to densities <26.6 kg/m 3, the North Pacific has a more much intense OMZ than all other oceans. In contrast, the subtropical southern hemisphere oceans south of 20°S are typically well ventilated with oxygen concentrations above 90 µmol Kg -1 , as a consequence of intense water mass formation over a wide outcrop area (Karstensen et al. 2008b).

The OMZ in the Costa Rica Dome covers the Central Water and the Intermediate Water layers. A typical upper boundary for the Intermediate Water in the North Pacific is at about density of 26.6 Kg m -3 . [START_REF] Tomczak | An analysis of mixing in the frontal zone of South and North Atlantic Central Water off North-West Africa[END_REF]) investigated the density range 25.2 -26.4 Kg m -3 in the Pacific. He described for the North Pacific the Central Water varieties of Eastern North Pacific Central Water (ENPW) and Western North Pacific Central Water (WNPCW). These water masses meet in the equatorial region to form Pacific Equatorial Water (PEW) which spreads eastward in a narrow band between the equator and 5° as a contribution to the Equatorial Undercurrent (Tsuchiya, 1981). A front at 5-10°N separates PEW from ENPCW east of 170°W. Certain density ranges are comparably well ventilated through the existence of Mode Waters embedded in the Central Water layer [START_REF] Karstensen | Formation of Southern Hemisphere Thermocline Waters: Water Mass Conversion and Subduction[END_REF].

The OMZ of the South Pacific reaches also suboxic conditions with minimum values only slightly higher than in the North Pacific. The lowest concentrations are found at depths of a few hundred meters, close to Peru. Warren (1995) found the oxygen consumption rate in the Peruvian upwelling system to be an order of magnitude higher than that in oligotrophic (low organic matter) water offshore. The large area extent of the OMZ is not just caused by the oxygen demand within Peruvian upwelling but also related to ocean circulation, as shown below.

Particularly low-oxygen content between 3°S and 17°S is observed along 88 °W the meridional section (Tsuchiya and Talley, 1998). The center of the OMZ is located near 400 m depth north of 10°S. A double minimum occurs south of 10°S with a shallower minimum at depth near 300 m. The thickness of suboxic conditions of less than 4.5 µmol Kg -1 reaches 400 m in the South Pacific. Bottle data show the existence of oxygen values lower than 1.5 µmol Kg -1 in the eastern part of the tropical South Pacific. At about 18°S the southern low oxygen domain is bounded by a sharp increase in oxygen of more than 40 µmol Kg -1 (1ml l -1 ) within 100 km (Tsuchiya andTalley, 1998 in Karstensen &[START_REF] Karstensen | Formation of Southern Hemisphere Thermocline Waters: Water Mass Conversion and Subduction[END_REF]

Eddy Kinetic energy (EKE)

The kinetic energy of mesoscale variability (tens to hundred of days and tens to hundreds of kilometers) is more than an order of magnitude greater than the mean kinetic energy over most of the ocean [START_REF] Wyrtki | Eddy energy in the oceans[END_REF][START_REF] Richardson | Eddy kinetic energy in the North Atlantic from surface drifters[END_REF]. Mesoscale variability occurs as linear Rossby waves and as nonlinear vortices or eddies. In contrast to linear waves, non linear vortices can transport momentum, heat, mass and the chemical constituents of seawater, and thereby contribute to the general circulation, large-scale water mass distributions and ocean biology (Robinson, 1983;[START_REF] Chelton | Global observations of large oceanic eddies[END_REF]).

Cyclonic and anticyclonic eddies characterized by negative and positive Sea Surface High (SSH) levels respectively are present in several regions, usually north of 5° N and south of 5°S (see introduction section) (Fig. 2

.14 ).

Several conclusions can be drawn from the observed distributions of mean and eddy kinetic energy. In regions where an orderly mean flow is essentially absent, substantial amounts of eddy energy are still present. Virtually nowhere have eddy energies of less than 200 cm 2 s -2 been observed, which means that even in the least energetic parts of the oceans, mean speeds of water movements in the surface layer are about 20 cm s -1 . Higher values of eddy energy, exceeding 1000 cm 2 s -2 are associated with strong ocean currents, in particular the western boundary currents. Such an energy level corresponds to a mean eddy speed of about 45 cm s -1 , which is surprisingly only about twice the speed in the central ocean areas. The increased values of eddy energy in the equatorial currents can be explained accordingly [START_REF] Wyrtki | Eddy energy in the oceans[END_REF].

Indian Ocean

From the distribution of the Eddy kinetic energy, it appears that the western boundary along the coast of Africa acts as a source and the northern regions (Arabian Sea and Bay of Bengal) as well as the regions south of 20°S as sinks [START_REF] Shenoi | Near-surface circulation and kinetic energy in the tropical Indian Ocean derived from Lagrangian drifters[END_REF].

Arabian Sea

The marked seasonal cycle in the surface forcing over the Arabian Sea leads to a strong seasonal cycle in the circulation of the western Arabian Sea. The consequence is the annual reversal of the Somali Current on the basin's western boundary [START_REF] Schott | Monsoon response of the Somali Current and associated upwelling[END_REF], with peak currents up to 3.7 m s -1 during the SW Monsoon. Observations have swhown a vigorous mesoscale eddy field in the Arabian Sea. The SW Monsoon wind field drives the upwelling along the Arabian coast, leading to observed persistent jets of cold upwelled water extending from the Oman coast, often reaching hundreds of kilometers into the interior (Brink et al., 1998;Fischer et al. 2002) (Fig. 2.15).

Mozambique Channel

Located between Mozambique and the island of Madagascar, the Mozambique Channel is dominated by poleward-travelling anticyclonic eddies that have diameters ranging between 300 and 350 km [START_REF] Biastoch | The importance of flow in the Mozambique Channel to seasonality in the Greater Agulhas Current System[END_REF][START_REF] Heywood | Retention of phytoplankton by an eddy[END_REF][START_REF] Ridderinkhof | Moored current observations in the Mozambique Channel[END_REF]. These eddies are formed north of the Channel (~15°S) with a frequency of 7 per year. This frequency decreases to 4 per year toward the southern end of the Channel due to dissipation or merging of eddies [START_REF] Lamont | Physical drivers of phytoplankton production in the southern Benguela upwelling system[END_REF]

Eastern Pacific Ocean

Fronts and eddies in the Tropical Pacific have different origins from the land sea jets until the coastal upwelling systems are favorable environments to generate these structures. Strong and persistent fronts, as previously described, have their origin in the equatorial region mainly due to the formation and seasonal movement of the cold equatorial tongue. Eddies are result of the coastal circulation as in the Peru Current system [START_REF] Chaigneau | Near-coastal circulation in the Northern Humboldt Current System from shipboard ADCP data[END_REF]) and the three narrow wind jets, Tehuantepec, Papagayo and Panama, are considered the most important agents generating eddies in the north of the Tropical Pacific region. [START_REF] Willett | Eddies and Tropical Instability Waves in the eastern tropical Pacific: A review[END_REF] quantified up to 18 cyclonic or anticyclonic eddies formed in this area. The authors describe the anticyclonic eddies as features with a diameter of ~ 180 and 500 km, lowering the pycnocline from ~60 to 145 m at the eddy center, with a swirl speed in excess of 1m.s-1 and a westward direction with velocities of 11 to 19 cm.s-1. Finally they observed a height signature up to 30 cm (Fig. 2.16).

Particularities of the current patterns in the Indian Ocean

Currents in the south of the Indian Ocean basin are more similar to the other subtropical oceans due to the limited influence of the monsoonal winds compared with the north of the basin. In the Northern Indian Ocean during the summer monsoon (SW-monsoon) in the Arabian Sea, there is an anticyclonic re-circulation to the south of the eastward vein, very likely forced by the curl of the winds stress. There is also some cyclonic eddies north of this circulation [START_REF] Fieux | Current system in the Indian ocean[END_REF]. The South Equatorial Current (SEC) and the East African Coastal Current (EACC) supply the northward Somali Current. Depending on the time of the season and driving wind field, the Somali Current can appear as a set of different cells and gyres [START_REF] Schott | The monsoon circulation of the Indian Ocean[END_REF]. After crossing the equator as part of the low-latitude Somali Current (SC), it turns offshore near 4°N, with a cold upwelling, and part recirculates across the equator as the "Southern Gyre". In the north, a second gyre is formed, the "Great Whirl". A third gyre the "Socotra Eddy" is seen in many summer monsoons northeast of Socotra. Elsewhere, the Southwest Monsoon Current (SMC) flows eastward south of Sri Lanka and then turns northward to bring saltier Arabian Sea water into the Bay of Bengal.

During the winter monsoon (NE monsoon), the Somali current flows southward. It meets the northward flowing (EACC) in a confluence zone from 2 to 4°S, supplying water for the eastward flowing South Equatorial Counter current (SECC). The SECC exists year round, except that during the summer monsoon it is subsurface, masked by overlying westward Ekman currents [START_REF] Han | modeling salinity distributions in the Indian Ocean[END_REF]. In the Arabian sea there is a general westward flow south of 10°N, known as the North-east monsoon current (NMC) that reaches 2°S. South of Sri Lanka the current splits into a westward branch and a branch that moves towards the western coast of India [START_REF] Fieux | Indian ocean equatorial currents[END_REF]).

In the southern Indian Ocean, the Indonesian Throughflow (ITF) supplies the South Equatorial Current that flows westward and splits in two branches at the east coast of Madagascar (~17°S). These two new currents are the Northeast and Southeast Madagascar Currents (NEMC and SEMC). The NEMC transport about 30Sv, supplying water for the Mozambique channel flow and the East African Coast Current (EACC). The southward branch transport about 20 Sv to the southern tip of Madagascar, where it is suggested that it dissolves into a sequence of eddies and dipoles that migrate to the African coast [START_REF] De Ruijter | Eddies and dipoles around South Madagascar: formation, pathways and large-scale impact[END_REF]).

Indian Ocean Dipole

It is a pattern of internal variability with anomalously low sea surface temperatures off Sumatra and high sea surface temperature in the western Indian Ocean, with accompanying wind and precipitation anomalies. The spatio-temporal links between sea surface temperatures and winds reveal a strong coupling through the precipitation field and ocean dynamics. This air-sea interaction process is unique and inherent to the Indian Ocean and is shown to be independent of the El Nino/Southern Oscilation. The discovery of this dipole mode that accounts for about 12% of the sea surface temperature variability in the Indian Ocean -and, in its active years, also causes severe rainfall in eastern Africa and droughts in Indonesia-brightens the prospects for a long-term forecast of rainfall anomalies in the affected countries [START_REF] Saji | A dipole mode in the tropical Indian Ocean[END_REF].

During the dipole mode events, the surface wind field over the Tropical Indian Ocean experiences large changes, especially in its zonal (east-to-west) component over the Equator. Maximum changes in the zonal wind occur over the equatorial central and eastern Indian Ocean. The intensity of the SST dipole mode and the strength of the zonal wind anomaly over the Equator are strongly dependent on each other (Fig. 2.17). La Niña is sometimes referred to as the cold phase of ENSO and El Niño as the warm phase of ENSO. These deviations from normal surface temperatures can have large-scale impacts not only on ocean processes, but also on global weather and climate (Fig. 2.18).. El Niño and La Niña episodes typically last nine to 12 months, but some prolonged events may last for years. While their frequency can be quite irregular, El Niño and La Niña events occur on average every two to seven years. Typically, El Niño occurs more frequently than La Niña.

EL Niño and La Niña conditions

El Niño event strongly impact the general phytoplankton productivity having effects all trophic levels and consequently on fisheries.

Chapter 3 3 Purse seine fisheries and tropical tuna species

This chapter presents the main characteristics of the Purse-seine fishery, its development in the Indian and Eastern Pacific Ocean and the type of sets frequently used to catch on tunas. The three main species of tropical tuna, yellowfin tuna (Thunnus albacares), skipjack tuna (Katsuwonus pelamis) and bigeye tuna (Thunnus obesus) are described, discussing aspects as their physical characteristics, their physiology and their commercial relevance.

Purse-seine Fisheries

Purse-seine fishing gear is defined as "a coin purse shaped net used for catching fish" (ISSF 2014). This large wall of netting is deployed around a school of fish [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF] and then the bottom is closed (Franco-Gonzales et al. 1998); [START_REF] Montero | Characterizing environmental and spatial variables associated with the incidental catch of olive ridley (Lepidochelys olivacea) in the Eastern Tropical Pacific purse-seine fishery[END_REF]. The net or "seine" has floats along the top with a lead line threaded through rings along the bottom (Fig. 3.1, 3.2). Once a school fish is located, an auxiliary boat or skiff encircles the school with the net. The lead line is then pulled in, "pursing" the net closed on the bottom, preventing fish from escaping by swimming downward.

The purse seine vessels have a broad range of sizes and capacities (from few tonnes to 3000 tonnes). The length reached for a vessels is 20-120 m [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. The net length may reach more than 2200 m and its depths are usually from 150 m to 350 m; the mesh size varies from 7.5 cm to 25 cm but the vast majority is of 10.8 cm stretched mesh (Figures 3.1,3.2). Most seiners carry an array of instruments to facilitate navigation and detection of tuna schools such as bird radars use to detect seabirds frequently associated with tuna schools. Another instrument is the echo sounder or sonar that provides information about the location and volume (tonnes) of the fish target [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF].

Indian Ocean

Before the expansion of the purse-seine fisheries in the Indian Ocean during the first half of the 1980s tuna was mainly fished with longlines and pole and lines gears. The bulk of the seine catch is composed of more or less equal amounts of yellowfin and skipjack tuna. A large proportion of the catch is taken by vessels from outside the region. French and Spanish fleets for example, alternate their operation between the area of Seychelles and the Atlantic Ocean, depending on the prospects. As result of these changes and trends, several new countries fishing in the Indian Ocean, such as Thailand, Philippines, Indonesia, India, Sri Lanka, Seychelles, Maldives, are developing their own tuna fleets and processing industries (Ben-Yami 1994); (FAO 2015)

Eastern Pacific Ocean

Several Latin American countries started developing tuna fleets, flying their national flags, to fish the Eastern Pacific tunas. This development has apparently been motivated by the proclamation of the 200 mile Economic Exclusive Zones (EEZs) and possibly by a series of conservation and labor regulations imposed on vessels flying the US flag. Among these countries are Peru, Chile, Ecuador, Panama, Venezuela and Mexico. Perhaps the most modern fleet of tuna purse seiners in the Eastern Pacific Ocean is that of Mexico, for which a large investment was made for its development in the late 1970s. Many tuna fisheries are present in Central and west Pacific Ocean. The US tuna purse seine fishery had been developed exclusively in the eastern Pacific and expanded all over the Pacific in the 1970s and 1980s. However, the use of traditional California tuna purse seines for skipjack fishing in the central and western Pacific was not an unqualified success. Japanese purse seiners fishing long, deep and light weight purse seines out-fished many American seiners. Giant seines with length of 2000 m and more have been used by Japanese tuna purse seiners in the western Pacific Ocean [START_REF] Ben-Yami | Purse seining, FAO fishing manuals[END_REF].

With the development and increasing sophistication of fishing with Fish Aggregating Devices (FADs) the need for such huge nets has diminished and the more conventional net is now about 1600 m long and 300 m deep. The Spanish fleet composed by large sized vessels is distributed among Pacific Ocean, Eastern and Central Atlantic. The tuna purse seining fleet of France was developed during the 1960s and nowadays is operating in Southern Pacific. At the same time, Taiwanese and South Korean seiners began vigorous operations in the south west Pacific waters. In the Philippines, tuna fisheries have achieved a remarkable rate of development during the 1970s and 1980s, partly as a result of the successful introduction of large-scale purse seining operations in association with FADs and log fishing. In this country most of the purse seine industry is based on a combined use of FADs of steel or bamboo construction and light attraction techniques. The method combines indigenous (FAD and light attraction) and modern (mechanized purse seining) technologies, which when operated separately would not be as productive. The FADs are set in ocean waters from 20 to 60 miles offshore and some 7 miles apart [START_REF] Ben-Yami | Purse seining, FAO fishing manuals[END_REF]FAO, 2015).

Set types

Different type of sets can be made during the purse seine operation depending on tuna behavior. Tunas are known to migrate long distances, sometimes associated with other species or different floating objects. These associations define the different types of sets used in the purse seine fishery. These are: school sets, dolphin sets, seamounts sets, whale sets, log sets, FAD sets, etc. [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]). Here we describe the type of sets most frequently used by the purse seiners in the Indian and Pacific Oceans.

Free school sets

In these type sets the detection of the school is possible thanks to the intense activity of the individuals near the surface. The vessel detects a disturbance on the ocean surface indicating the tuna presence, sometimes related with the presence of seabirds. This occurs when tuna is in a feeding frenzy or other type of very active behavior close to the surface that has caused the disturbance. This fishing mode is used to describe the behavior of tuna when it is not affected by an association. An important characteristics of this type of sets is that they tend to be mono specifics or clearly dominated by one tuna species. Some of the names that the fisherman use to described tuna behavior in the school sets are: Breezers, jumpers, boilers and foamers [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF].

Dolphin sets.

Aggregations between yellowfin tuna (Tunnus albacares) and dolphins of the genera Stenella (S. attenuata and S. longirostris) and Delphinus (D. delphis) are very common in the Eastern Pacific but very rare or absent in other oceans [START_REF] Donahue | An Annotated Bibliography of Available Literature regarding cetacean Interactions with Tuna Pure Seine Fisheries Outside of the eastern Tropical Pacific[END_REF]; [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. Tuna fishermen have exploited this association for many decades because dolphins are easier to sight at large distances and make the tuna swimming beneath them easier to follow and catch [START_REF] Scott | Pelagic predator associations:tuna and dolphins in the eastern tropical Pacific Ocean[END_REF].Tuna in this association was initially caught by the baitboat fishery that used live baitfish in order to attract tuna to the surface. By the mid 1960's, however, the this fishery was transformed into a purse seine fishery as the dolphins ware no longer used just to find the tuna, but were actively chased and encircled to catch the tuna [START_REF] Scott | Pelagic predator associations:tuna and dolphins in the eastern tropical Pacific Ocean[END_REF]. This kind of fishing was temporarily reduced in the early 1990s owing to tuna safe fishing requirements by the US and the European markets, leading to increase in log (natural objects) sets and free school fishing [START_REF] Hall | On bycatches[END_REF]. This fishery is practically monospecific for yellowfin tuna and the sizes of tuna caught are quite large, with the vast majority being more than 80 cm in length, with a average weights in the different regions of 14-31 kg (IATTC data; [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. Despite the long history of this type of sets, there are still questions about the biological basis for this association [START_REF] Scott | Pelagic predator associations:tuna and dolphins in the eastern tropical Pacific Ocean[END_REF]

Log sets

The origin of the fishing operation on floating objects is known to start as an opportunistic activity whenever an object was encountered [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF] Fishers observed the aggregations of tuna and other species with trunks and branches of trees and also with plants in regions with an important input of organic matter from the continent. Especially productive were the coastal waters in regions where there were significant inputs from the continent, such as those with abundant forest, and tropical rivers that could carry a lot of material during the floods that mark the beginning of the rainy season [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF] . These events occur in the regions where the Inter tropical convergence zone intersects the continents [START_REF] Scott | Proceedings of the international workshop on the ecology and fisheries fro tunas associated with floating objects[END_REF]. In these regions tree trunks and branches, aquatic plants and other materials coming from land ware carried out sea, and the tunas present in those areas associated with them [START_REF] Caddy | Tuna and trees: a reflection on a long-term perspective for tuna fishing around floating logs[END_REF]; [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. [START_REF] Hall | Estimating the ecological impacts of fisheries: what data are needed to estimate bycatches[END_REF] described the types of floating objects observed in the Eastern Pacific Ocean during 1987 and 1990. They were, plant materials (tree, trunks, branches, etc), mostly unidentified trees, bamboo and other canes, palm trees and mangrove trees. Kelp patties were predominant in the California current system. Two other categories of objects were also described, objects of anthropomorphic origin (crates, pallets, lost fishing gears, etc) and of animal (dead animals, including whale sharks, sharks, whales and pinnipeds as the main components) [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]. For the Indian ocean, [START_REF] Stretta | Les espèces associèes aux pêches thonières tropicales[END_REF]) reported that tree trunks and branches were the more common objects found.

Fish Aggregating Devices (FADs)

FADs are drifting or anchored buoys or rafts that attract and aggregate pelagic fish, making them easy to find and catch. It is known that FADs do not increase the abundance of fish, but only redistribute them into a specific and sometimes small area . There are thousands of FADs in use worldwide and other more waiting to be deployed. The purse seine fishery uses this method to fish, but also handline and pole and line fisheries use them [START_REF] Beverly | Anchored fish aggregating devices for artisanal fisheries in South and Souteast Asia: benefits and risks[END_REF]. FADs can be anchored or drifting objects. They are made of different materials but most of FADs share common components as floating elements, (usually bamboo), ropes, netting material and some weight [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF] A well known type of anchored FAD is an object using palm leaves to provide an attractive structure, called a "Payao" for its Philippine name (Fig. 3.3). They are commonly used by artisanal and sport fishing fleets but also by industrial pole and line and tuna purse seine vessels. These FADs are especially important in regions such as Papua New Guinea and in the Philippines, where they are used by artisanal and industrial fleets. They are used in different locations mainly in the Western Pacific Ocean (WPO) and Maldives in the Indian Ocean [START_REF] Hall | Bycatch and non-tuna catch in the tropical tuna purse seine fisheries of the world[END_REF]ISSF 2015).

FADs that drift freely in the ocean after being deployed are know as "Drifting FADs" (Fig. 3.4). They are deployed for the exclusive use of the boat or fleet that set them afloat, but if they are found by other vessels they can be set on. Recently fishers have been improving FADs with new technology (mainly on drifting FADs) as radio buoys (sometimes called FTBs or FAD tracking buoys) which send radio signals on pre-set frequencies known only by the owner and boat captain. FADs can be tracked using the transmission of their positions, with the frequency selected by the deploying skipper (daily, several times a day, etc).

FADs Association hypothesis

Drifting objects floating in the ocean tend to be accumulated within convergent frontal structures [START_REF] Bakun | Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage[END_REF]). However, a widespread attraction of any type of fish to these objects is cited as an apparent adaptation response to the desirable aspects of surface fronts. This attraction and aggregation behavior has been observed by fishermen in different fisheries that use natural floating objects such as logs, or man-made FADs adrift or anchored in convenient locations in order to attract and concentrate fish, thereby enhancing fishing success. Several fisheries exploit this behavior [START_REF] Bakun | Fronts and eddies as key structures in the habitat of marine fish larvae: opportunity, adaptive response and competitive advantage[END_REF]). An innate attraction to drifting objects would serve to automatically position the fish within such convergent zones where enhanced biological activity may lead to improved probability of encountering favorable feeding opportunities.

In order to explain this aggregating behavior and their positives and negative effects on fish population some hypothesis have been raised [START_REF] Hall | Efficiency of weighted averages[END_REF]) discussed the hypothesis related to the association of tuna with floating objects and dolphins. One of the most important and defined as a good approach to the reality is the "Indicator log" hypothesis, defining the tuna association with logs as an adaptive process that keeps the tunas in rich waters. In other words, logs act as indicators of rich waters because 1. most of the natural floating objects originate in rich areas (mouths of tropical rivers, mangrove swamps, etc); 2. they are retained in the coastal zone for some time by the current systems, and eventually drift offshore with their original, enriched water mass; and 3. they eventually aggregate in frontal zones.

Another hypothesis proposed to explain tuna aggregation around floating objects is the "Meeting point hypothesis" [START_REF] Fréon | Review of fish associative behaviour: Toward a generalisation of the meeting point hypothesis[END_REF] . This hypothesis suggests that tuna can use this objects to form large schools after school fission or dispersion during feeding. [START_REF] Fonteneau | A worldwide review of purse seine fisheries on FADs[END_REF] stated that behaviorism tendency of Tuna to associate with FADs may have additional effects on tuna population and fisheries, such as: loss of genetic quality, negative interaction between the offshore industrial fisheries using drifting FADs and the artisanal ones using anchored FADs and that FADs may function as an ecological trap. About an "ecological trap" hypothesis, [START_REF] Marsac | Drifting FADs used in tuna fisheries: an ecological trap? In Pêche thonière et dispositifs de concentration de poissons[END_REF] distinguished three important components in the association with FADs: 1. The aggregation of fish under FADs is a fast, strong and long-lasting process. 2. The large number of drifting FADs in the equatorial zone, can alter the natural movements of this fraction of stocks and 3. Subsequently, drifting FADs negatively affect growth and natural mortality of small tuna associated with. The author concluded that the massive deployment of FADS in the ocean could have serious implications in terms of evolution, ecology and conservation of resources. Based in this work (and using tagging data) [START_REF] Hallier | Drifting fish aggregation devices could act as an ecological trap for tropical tuna species[END_REF] suggested that tuna might be trapped around man-made FADs , having consequences as an incorrect habitat selection and unfavorable effects on tuna health.

Purse seine Fish Operation

The purse seine fishing operation consists of several steps: 1) searching for fish aggregations with the help of radars and birds sightings, evaluating the fish school sizes and their catchability. 2) deploying the net with the help of an auxiliary skiff 3) setting the net around the detected school. 4) closing the net underneath the school 5) hauling the net by pulling the purse cable and picking up the rings from the lower end (Fig. 3.5). When the process of encirclement is finished the end of the net that had remained attached to the skiff is transferred aboard the purse seiner and the two ends of the purse cable are hauled with the winch as quickly as possible in order to close the net at its bottom ("pursing"). It is worth observing that, until the purse seine is not closed, the tunas can still dive below the net or the purse seine vessel and escape. If the net extends all the way from the surface down to the thermocline, the chance of fish escaping through the bottom would be reduced. During pursing, especially when there is a current, the skiff is attached to the starboard side of the vessel, where it can pull it away from the net in order to prevent the purse seiner from drifting over the net. The time of activity for large purse seiner is around 15 to 20 minutes (Ben-Yami 1994; Hall & Roman 2013). Yellowfin tuna catches in the tropics by the purse seine has been correlated with the strength of the thermocline, indicating temperature to be the major limiting factor in their vertical distribution [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF][START_REF] Brill | A review of temperature and oxygen tolerance studies of tunas pertinent to fisheries oceanography, movement models and stock assessments[END_REF]). An additional factor that can also limit its vertical distribution is the dissolved oxygen, with values around 3.5 ml l -1 [START_REF] Brill | A review of temperature and oxygen tolerance studies of tunas pertinent to fisheries oceanography, movement models and stock assessments[END_REF].

Regarding yellowfin tuna reproduction, studies based on the simultaneous observations of the macroscopic characters of gonads, of the diameter of oocytes and the evaluation of the gonad index, allow to distinguish different phases in the sexual cycle of this tuna that is, the maturation of the gonads from October to December and spawning from December to April. A secondary reproductive period may also exist in July-August in the eastern part of the Indian Ocean between the Seychelles and Chagos Islands [START_REF] Hassani | Sexual maturity, spawing and fecundity of the yellowfin tuna (Thunnus albacares) of the Western Indian Ocean[END_REF]; [START_REF] Lumineau | Study of the growth of yellowfin tuna (Thunnus Albaares) in the Western Indian Ocean based on length frequency data[END_REF]. Adults individuals feed on fish, squid and crustaceans. In the other hand, larval and juvenile tuna can be prey by fish, seabirds and other animals. Marine mammals, Billfish and sharks may feed on adult Yellowfin (NOAA 2015).

Yellowfin form free-swimming and associated schools, are generally adults of similar size. These freeswimming schools tend to be mono-specific and to contain large individuals. Small individuals are caught on the surface and are used mainly for canning. The majority of large organisms are shipped fresh (ISSF 2015) This specie also gathers around drifting logs (natural floating objects), fish aggregating devices (FADs), anchored buoys, dolphins (mainly in the Pacific Ocean) and other dead or alive animals as whales. Catches on Dolphins represent the highest percentage of captures among all the type sets for this species. As with many tropical tunas species, environmental conditions are believed to significantly influence recruitment levels over time.

In the Pacific Ocean, according to ISSF (2015) there are two stocks of Yellowfin tuna one in the western and central Pacific and one in the Eastern Pacific. In the western-central Pacific, tuna is harvested in small-scale artisanal fisheries in the islands and southeast Asian waters as well as by long-liners and purse seiners in the equatorial and tropical waters. In the eastern Pacific this tuna is mainly harvested by purse seiners from Central and South America.

Skipjack tuna (Katsuwonus pelamis (Linnaeus, 1758))

An epipelagic and oceanic species, skipjack tuna is mainly found in waters with a surface temperature range between 24 and 32°C in the Atlantic, Pacific and Indian Oceans. Equatorial ecosystems or their warm coastal derivatives, such as the Kuroshio and the Brazil currents, represent their favorite habitat [START_REF] Fonteneau | A comparative overview of skipjack fisheries and stocks world wide[END_REF]) although a small percentage of skipjack is caught in temperate waters between 18 and 24 °C (e.g., in New Zealand and Azores) [START_REF] Dueri | Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution[END_REF]. Its vertical distribution can reach 260 m during the day and the surface waters during the night (Collete et Nauen, 1983). Regarding the dissolved oxygen, different values have been reported, Cayre (1987) established a range of values between 3.0 and 3.5 ml l -1 while Levenez (1982) found values around 1.5 ml l -1 during his study when this specie reached depths around 400 m. This specie do not follow clear spawning or feeding migration patterns [START_REF] Stéquert | Reproduction of skipjack tuna(Katsuwonus pelamis) from the western Indian Ocean[END_REF] but rather exhibits home range movement within habitat preferences. Environmental conditions as prey availability, temperature and dissolved oxygen concentration affect the skipjack tuna distribution, movements and catchability (Dueri et al. 2012).

Skipjack preys on fishes, crustaceans and mollusks; they can also be opportunistic feeders, preying on any forage available. They prey during the early morning and late afternoon. Cannibalism is common. Billfihes and other tunas prey on skipjack. This species is principally associated with Fish Aggregating Devices (FADs), but they can also be found in free (un-associated) schools. When associated with objects they are frequently caught with juvenile yellowfin and bigeye tunas. They are the smallest of the major commercial species. Skipjack is caught mainly on the surface by purse-seine and pole and line gear being the primary species in canned tuna.

In the Indian Ocean, skipjack tuna catches represent almost half of the total catch of tropical tuna. Although the skipjack population is considered to have a high resilience against overfishing, due to fast growth rate and the year round spawning, the decreasing trend of the surface fisheries catches since 2006 led to concerns about the sustainability of the present level of exploitation [START_REF] Adam | Declining catches of skipjack in the Indian Ocean-observations from the Maldives[END_REF]Dueri et al. 2012a).

Nowadays, skipjack tuna catches have reached 2.5 million tons and represents the largest contribution in terms of weight to the total world tuna catches. Roughly 65% of skipjack catch comes from the Western Pacific Ocean, 20% from the Indian Ocean and the rest (15%) is shared nearly equally between the Atlantic Ocean and the Eastern Pacific Ocean (data from Indian Ocean Tuna Commission, 2011; International Commission for the Conservation of Atlantic Tuna, 2012; Western & Central Pacific Fisheries Commission, 2010; and Inter-America Tropical Tuna Commission, 2012) [START_REF] Dueri | Projecting the impacts of climate change on skipjack tuna abundance and spatial distribution[END_REF]).

In the eastern Pacific, the greatest component of the catch of the FAD fishery is skipjack [START_REF] Bayliff | Annual report for 2001. Inter-American Tropical Tuna Commission[END_REF]. Catches of this species have occurred from about 34°N off southern California to about 27°S off Chile. Skipjack is limited to temperatures between 17° to 30°C (Schaefer and Fuller, 2007). This species is one of the three principal targets of the purse seine fishery in the Eastern Pacific and is caught mostly in association with floating objects between about 10°N and 15°S. Catches of skipjack by purse seiners during 1995-2004 averaged 197 000 metric tons (t), which is 150% greater than the average of 76 000 t caught during the previous 10 years (Anonumus, 2005;(Schaefer & Fuller 2007).

Bigeye tuna (Thunnus obesus (Lowe, 1839)

An epipelagic and mesopelagic species is mainly found in oceanic waters [START_REF] Laevastu | Distribution and relative abun-dance of tunas in relation to their environment[END_REF][START_REF] Kikawa | Maturation and spawning of tunas of Indian Ocean[END_REF][START_REF] Carocci | Pacific tunas and billfishes: atlas of commercial catches. Food and Agricultural Organization[END_REF][START_REF] Fonteneau | Tuna natural mortality as a function of their age: the bigeye tuna (Thunnus obesus) case[END_REF][START_REF] Lee | Spatial and Temporal Distribution Patterns of Bigeye Tuna (Thunnus obesus) in the Indian Ocean[END_REF]. Their eurybathymetric distribution makes it difficult to relate their horizontal distribution and movements to precise surface environmental ranges. Indeed, the physiological ability of adult bigeye to regulate their body temperature (Graham and Dickson, 2001) and their tolerance to low dissolved oxygen levels < 1.5 ml l -1 (Brill, &994) allow them to colonize deep cold waters during the day to feed in the prey rich sound scattering layer (SSL) [START_REF] Josse | Simultaneous observations of tuna movements and their prey by sonic tracking and acoustic surveys[END_REF][START_REF] Dagorn | Movement patterns of large bigeye tuna (Thunnus obesus[END_REF][START_REF] Bertrand | Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability[END_REF][START_REF] Bach | Acoustic telemetry versus monitored longline fishing for studying the vertical distribution of pelagic fish: bigeye tuna (Thunnus obesus) in French Polynesia[END_REF][START_REF] Maury | APECOSM : a spatialized, physiologically structured apex predators ecosystem model[END_REF].

The vertical and horizontal distributions of bigeye tuna are closely related with the temperature and the thermocline depth. An optimum range of temperature seems to be 17° -22°C that coincide with the range of temperature of the permanent thermocline. Major concentration of this species in the western-central Pacific are related to seasonal and climatic changes in surface temperature and thermocline (ISSF 2015).

Adult individuals occupy the first 100 m of the water column at night and exhibit a much deeper distribution (~ 250 to 500 m depth) during the day. They forage in the SSL below the thermocline and punctuate their dives by brief upward excursions to warm their body above the thermocline before diving again into the SSL. Recently, the development of electronic archival tagging experiments [START_REF] Gunn | Advances in acoustic, archival, and satellite tagging of tunas[END_REF][START_REF] Schaefer | Movements, behavior, and habitat selection of bigeye tuna (Thunnus obesus) in the eastern equatorial Pacific, ascertained through archival tags[END_REF][START_REF] Musyl | Vertical movements of bigeye tuna (Thunnus obesus) associated with islands, buoys, and seamounts near the main Hawaiian Islands from archival tagging data[END_REF] has enabled the generalization of those results to establish that the diurnal diving behavior of adult bigeye tunas is characteristic and has been observed in various regions and different oceans. It should nevertheless be noted that this "normal" behavior is sometime interrupted by periods where the tuna doesn't dive and stays days and nights in surface waters [START_REF] Maury | APECOSM : a spatialized, physiologically structured apex predators ecosystem model[END_REF].

Juveniles and small adults of Bigeye tuna are normally found at the surface in mono-species groups or together with Yellowfin and Skipjack associated with floating objects. Schools of Bigeye tuna can be associated with large, slow-moving marine animals such whales, sharks or manta rays. They also gather near seamounts and submarine ridges.

In the Indian Ocean, bigeye tuna constitutes an extremely valuable fishery resource intensively exploited by longliner vessels and purse seiner vessels at various stages of life cycle. In the mid 2000s, Indian Ocean purse seine landings of bigeye tuna were essentially composed of small immature fishes (<90 cm Fork-Length) [START_REF] Sun | Age and growth of the bigeye tuna, Thunnus obesus, in the western Pacific Ocean[END_REF].

Bigeye tuna are caught in the eastern Pacific Ocean (EPO) primarily by Asian distant water longline vessels targeting them from about 30°N to 30°S, and by industrial purse-seine vessels, of many nations, targeting tuna aggregations associated with drifting floating objects, mostly FADs, from about 5°N to 15°S (Anonymous 2008). Since 1994 catches of bigeye from longline fisheries have significantly decreased and those of the purse seine fisheries have significantly increased. The purse seine fishery captures smaller bigeye, and the consequences of that fishery over the past 16 years include reductions in the yield per recruit and maximum sustainable yield. Because the stock of bigeye in the EPO is overfished, there is uncertainty about the sustainability of recent levels of exploitation (Anonymous 2008; Aires-da-Siva and Maunder 2009; [START_REF] Schaefer | Vertical movements, behavior, and habitat of bigeye tuna (Thunnus obesus[END_REF]. tools as seasonal maps, by quarters (Fig. 4.1), 2) Statistical analysis exclusively developed for this subject.

Due to the registering of the species composition by size category (fishes smaller or lager than 10 kg) in the original log books, the processing of the data through the sampling method allows to estimate for each set the corrected species composition (as described by [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF])

As a result of the method applied and the analyses made for the Indian Ocean, 5 spatial strata have been defined for the correction of the species composition (Fonteneau et al 2007;[START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF]: Somalia, Western Seychelles, Eastern Seychelles, south Seychelles and Mozambique Channel. Two fishing modes were also defined through the school typology. This implied low species diversity figures and high average weights as the criteria for definition of Free School and high diversity indices with low average weights for Log school. The seven sighting codes analyzed were then reclassified in two groups being 1) Log group: object, whale-shark and under vessel fish association and 2) Free School group: free school, marine mammal, sighting code and unknown. As stated by [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF], the sighting code and unknown categories are assigned to the free school groups based on resemblance to the two reference groups.

The more recent data used in our study consider 4 categories were "Associated objects" has been reclassified as:

1) Log-associated school 2) Free school 3) undetermined school 4) Mixed school as defined in the reference information of the data base.

The term "Object" (thereafter named "FAD") is used to mention an object either fully build by humans, or natural (as a log) either equipped with a tracking device or not (object of opportunity) , or a large living organism (as whale or whale sharks). France. This database created by (Fonteneau et al 2007) is derived from the original sampling files of multispecies sizes samples [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF]). The data base provides for each sample a summary of the observed species and size composition (small and large yellowfin and bigeye tuna weight of each species in each sample), for all the tuna species landed, counted or measure. This data was obtained for the period 2003-2010. As mention before this data is the result of the species correction done on the combined analysis of log book and species and size sampling data collected during landings.

It is important to mention that in this study the size of individuals for YFT and BET, are separated in juveniles (small) and adults (large) and that SKJ are in a unique weight category (very small individuals). These categories make part of the results obtained by Fonteneau et al (2007), who observed that skipjack catches were always < 10 kg. He also observed that yellow fin tuna (YFT) catches on FADs were < 10 kg while catches on free school were mainly > 10 kg. Finally for Bigeye tuna (BET) catches on FADs were < 10 kg whereas catches of this species on Free schools tend to be > 10 kg (Fig. 4.2). The SPECIES data base also contains information about the total catches the sets position (latitude, longitude) and the date (year, month, day) of all sets made on tropical tuna. The catches in the Indian Ocean used in this study were mainly made by the French fleet on free school and FAD/object associated fishing modes. Additionally to the characteristics of all sets, the data include a detailed description of the correspondent vessels as their capacity and year of service among other parameters (see Fonteneau et al 2007 for more details). For this study we select only single sets by vessel in order to increase the precision in the relationship between catches positions and the environment describing it. We retained only the three main tuna species: yellowfin (YFT), skipjack (SKJ) and bigeye (BET).

Tropical tuna relative abundance

The relative abundance of the three species during the period of study (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) is shown in figure 4.5. YFT strongly dominates the catches in the Indian Ocean on Free Schools, whereas the 3 species are almost equally partitioned on FADs. Figure 4.6 show the total catch of the tree species during the study period and figure 4.7 to 4.9 the sets distribution by species and by type of set or fishing mode.

Seasonality of the catches

The seasonality of the catches in the Indian ocean is very pronounced and is taken into account in the "environment" models (Chapter 6 and 7) by introducing a "season" variable. The underlying hypothesis is that, independently of the environment, changes may occur in the environmental preferences of the individuals according their biological condition, as during the spawning season.

Because the seasonality in the Indian ocean is constrained by the Indian monsoon, the season splitting may have an influence compared to the administrative seasons (defined by quarters) used in the fishing data bases. For this reason, we explore the effective differences of the catch levels between the true climatic seasons (NE monsoon from December to March, intermediate monsoon I. from April to May, South-west monsoon SW from June to September and Intermediate season II from October to November ) with the administrative seasons. The differences are finally moderate, so the climatic splitting in quarters is kept in the following analyses (Fig. 4.1).

As already known, the spatial seasonality of the catches by the pure seiners in the Indian Ocean is well pronounced with a progressive change from the north of the Mozambique Channel from the second quarter (Q2) towards Somalia (maximum during Q3) and then a shift towards lower latitudes (Q4 and Q1) (Fig. 4.1). As a preliminary analysis prior to test the environmental effects on the 3 species we quantify the percentage of presence/absence for each species.(Fig. 4.10). YFT relative absence is only of 5% whereas the two other species are relatively well balanced (close to 50% / 50%) in terms of presence/absence. (See also appendix B).

Eastern Pacific Ocean

Single sets data from the Eastern Pacific Ocean (EPO) were supplied by the Inter-American Tuna Commission (IATTC), through a scientific agreement with the IRD-EME research Unit. The data is corrected at species and composition level, as previously mentioned in section 4.1.1 and correspond to some the areas declared by the observers. The study period correspond to the catches during 2007 and 2008, years without El Niño and with a moderate La Niña event respectively(Fig. 4.11). The goal is to minimize climatic influences in order to evaluate "normal" conditions associated to the species.

The IATTC data base contains the sets identification, the sets locations, the date of the activities, the species composition and the tuna size in weight by set type (or fishing mode type). Figure 4.12 show the relative abundance for YFT, SKJ and BET in the EPO during the period of study.

Tropical tuna relative abundance

In the EPO the association between tuna and dolphins, specifically of yellowfin tuna, is well known. This type of set represent the highest abundance for YFT among the all type of sets (~90%, IATTC). Due to the missing data for this type of set in this study figure 4.12 shows the species abundance on free school (FS), FADs and LOG sets.

All sets together show the highest abundance for SKJ (48%) followed by YFT (31%) and BET (21%). When we observe the type of sets separated, this difference is more important in FS where the abundance of SKJ corresponds to 68% while BET shows a very low percentage of 2%. FADs sets show a more balanced abundance between the three species, YFT (31%), SKJ(41%), BET (28%). For LOG sets SKJ still have the highest percentage (49%) contrasted with the minor percentage for BET (14%), while the intermediate abundance corresponds to YFT (37%).

Capture data from both bases were processed in order to have a standard catch in metric tonnes for both oceans and the dominance of the species depending of each type of fishing mode.

The splitting in size is very different of those observed in the Indian Ocean: for the EPO, the tuna species are recorded in metric tons by weight-size category (Fig. 4.14 -4.16). The three categories are:

• Small size, individuals < 2.5 kg • Medium size, individuals > 2.5 < 15 kg • Large size, individuals >15 kg.

Tropical tuna presence / absence

The relative Presence/absence of the 3 species in the EPO is quite different to those in the Indian Ocean in the sense that no species is strongly dominant as the YFT in the Indian Ocean. In particular, it is interesting to note that SKJ and BET have partially different habitats, specially in the coastal region from Colombia to Peru, whereas the YFT has a more homogeneous habitat (Fig. 4.16) (See also appendix B).

The spatial dominance between species shows again that the coastal Humboldt region is mostly dominated by SKJ, whereas BET catches are always located further offshore. The most homogeneous species is by far YFT, whose presence is not associated to a particular ecosystem.

Satellite data

As described in [START_REF] Chassot | Satellite remote sensing for an ecosystem approach to fisheries management[END_REF] satellite data represent a major advance in determining numerous characteristics of the marine surface environment, from the physics (Wind, currents, Sea surface Temperature (SST), surface topography (from which currents are partly deduced) to the biology as the sea surface chlorophyll (SSC), a proxy of the phytoplanktonic biomass, from which the primary production can be estimated.

Nowadays, all these parameters are derived from many sensors on board of many satellite platforms, either geostationary or polar orbiting, sometimes combined into synthetic products that offer a more continuous daily and "cloud-free" observation of the ocean surface. Because of the absence of mesoscale structures (i.e., front, eddies) due to the use of guess fields (often based on climatologies or other temporal averages) as well as destructive techniques in this regard (as kriging or other interpolation techniques) these combined products are not used in the present study.

For this reason, we used he SST and SSC from the MODIS sensor on board the Aqua spacecraft. This sensor has the advantage of supplying simultaneous observations (therefore with similar data gaps due to cloud presence) for SST and SSC.

Along these "classic" surface variables, we are specially interested in deriving parameters such as fronts and eddies. In particular, because fronts have been very often associated with tuna concentrations (see Introduction). This is why a specific method to derived and classify accurate fronts on ocean basins has been set (described in the following Chapter 5) in order to derive frontal statistics at large scale.

Sea surface temperature (SST) and Sea surface chlorophyll (SSC)

Daily SST and SSC fields were obtained from the Moderate Resolution Imagin Spectroradioeter (MODIS) of the Aqua platform for the period 2003-2010 (http://oceancolor.gsfc.nasa.gov/), at a 4.5 km spatial resolution. In order to increase the number of concordance with fisheries data, a very critical point in this study, the daily fields have been temporally interpolated on a 3-day period. This has been preferred to 5-day averaging, in order to be as close as possible to the environmental data, that do not vary too much over 48 hours, which is the time lap between the most separate days in this case.

Eddy kinetic energy (EKE)

Seven-day global altimeter products as sea level anomalies (SLA) and Eddy kinetic energy (EKE) were used, they are produced by Ssalto/Duacs and distributed by Archiving Validation and Interpretation of Satellite Oceanographic data AVISO, with support from CNES (http://www.aviso.altimetry.fr/duacs/). The Eddy kinetic energy is computed as follow: EKE = ½ (u 2 +v 2 )

Wind speed (Ws)

The daily wind speed used here is a Cross-Calibrated Multi-Platform wind product that combine cross-calibrated satellite winds using a Variation Analysis Method (VAM) to produce a high resolution (0.25 degree) gridded analysis. This data were obtained from the Physical Oceanography Distributed Archive Center of NASA (http;//podaac.jpl.nasa.gov/Cross-Calibrated_Multi-Platform_OceanSurfaceWindVectorAnalyses).

Sea surface chlorophyll dynamics (SSC_Dynm)

Sea surface chlorophyll-a from the day of the catch (d0) to 15 days before (d15) was computed using a temporal and spacial kernel box was moved backwards by 1-day time step. The most convenient spatial kernel size selected after different tests was 2 pixels (Fig. 4.21).

The daily dynamic of the chlorophyll a (from Level 3 SeaWiFS data at 4km resolution) has been analyzed from a sample of 114 catches (all tuna species) from 2001 to 2002. observations with insufficient data or insignificant trends have been removed.

Among the 94 remaining observations, 54% show a clear increase the day preceding the catch event, associated to a relatively high level of chlorophyll a and 46% a weak increase, associated with a very moderate decrease (and average level) of the chlorophyll concentration (Fig. 4.22).

Distances to fronts (Dist. Fronts)

Images of frontal distances are created to estimate the distances from the single catch sets to the "weak" or "strong" thermal fronts [START_REF] Roa-Pascuali | Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application[END_REF] as described in chapter 5. Here, based on the front persistence generally estimated on more than 1 day, we also apply the daily frontal images smoothed using the surrounding days (one before and one after) of the day of the catch. The objective of this technique, as in most variables presented before, is to increase the number of match with tunas catches (Fig. 4.24). We use 1 day images in order to observe all fronts It is important to note that:

• In some areas, high distances to fronts (> 400-500 km) are considered as spurious distances because of the potential lack of information due to the cloud cover..

The calculation is made independently for weak intensity and strong intensity fronts (measured by the local gradient of SST at the front location), as described in the Chapter 5.

Analysis of the spatial aggregation of the catches

We found that a random dispersion of the exact catch locations of approximately 250 km is necessary to obtain a quasi spatial uniformity. It is interesting to note that this dispersion range is close to the average size of the mesoscale structures found in the open ocean, mostly dominated by mesoscale eddies, except very close to the equator. This hypothesis is extensively tested using with a description of the environment based on key variables that adequately describe this mesoscale structures, in particular distances to eddies borders and to thermal fronts.

In order to test the importance of this variables, a randomly distributed set of positions is generated, that almost uniquely differ by its mesoscale component (<250km) (Fig. 4.25,4.26,4.27).

Here, we assume that the exact locations of the catches (corresponding to the activity of the fishery) are not evenly distributed in their location area.

Statistical Analyses

Models used to describe tuna environment

Most description of tuna distributions have used environmental variables with a range of different models to analyze the fisheries data components typically present in such studies. In some cases, combinations of several different models have been used, in order to increase the overall accuracy of the results. For tuna fisheries, one of the most frequently used model include the Generalized Additive Models (GAMs) (Hastié and Tibshirmi, 1990). This family of models comprises a collection of nonparametric and semiparametric regression techniques that are useful for exploring the relationships between response and predictor variables. For example, [START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF] used a combination of GAM and GIS mapping tools to study skipjack tuna habitat in the Northwestern Pacific Ocean. Meanwhile, [START_REF] Sagarminaga | Spatio-temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment[END_REF]) used a GAM modeling approach to evaluate the relationship between albacore tuna (Thunnus alalunga) presence and temperature-related environmental variables in the North Atlantic Ocean. [START_REF] Lan | Relationship between albacore (Thunnus alalunga) fishing grounds in the Indian Ocean and the thermal environment revealed by cloud-free microwave sea surface temperature[END_REF]) used a GAM model to predict the spatial patterns of potential YFT habitats from longliner data,with SST and net primary production as the predictor variables. In another study, GAMs were used to investigate the relationships between environmental variables (e.g., salinity and SST) with catch per unit effort (CPUE) data for YFT in the Atlantic Ocean [START_REF] Maury | Hierarchical interpretation of nonlinear relationships linking yellowfin tuna (Thunnus albacares) distribution to the environment in the Atlantic Ocean[END_REF]. More recently, these models have been used to determine the habitat preferences of six different tuna species in multiple oceans, using environmental data generated by the PISCES biogeochemical model (Arrizabalaga et al. 2015a). Overall, the advantage of using GAMs is that there is no need to make any a priori assumptions regarding the functional form linking the two set of variables, as these relationships are modeled with smooth functions.

In a slightly less exploratory way, Generalized Linear Models (GLMs) are also commonly used to analyze the effect of different factors on variables describing tuna fisheries (Nelder and Wedderburn,1972). For example, a GLM has been used to investigate the relationships between albacore tuna in New Caledonia and their environment, using a suite of variables including primary production [START_REF] Briand | A study on the variability of albacore (Thunnus alalunga) longline catch rates in the southwest Pacific Ocean[END_REF]. In another example, a GLM model was used to standardize CPUE data obtained from longline catch records for YFT in the western IO [START_REF] Lan | Effects of climate variability on the distribution and fishing conditions of yellowfin tuna (Thunnus albacares) in the western Indian Ocean[END_REF]. In some cases, researchers use GAM and GLM in combination. Such an approach was taken to predict the potential habitats of albacore tuna in the North Pacific Ocean using SST, SSC and SSH as the environmental predictors [START_REF] Zainuddin | Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data[END_REF]. For a more comprehensive overview of the modeling applications used in fisheries we can refer to [START_REF] Venables | GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research[END_REF].

In addition to the more popular models used, there are a number of less frequently used modeling approaches. For example the Spatial Environmental Population Dynamics model (SEPODYM) which is based on a diffusion-advection equation in two horizontal dimensions was used to observe the effects of El Niño events on skipjack tuna populations in the tropical Pacific Ocean [START_REF] Lehodey | The pelagic ecosystem of the tropical Pacific Ocean: dynamic spatial modeling and biological consequences of ENSO[END_REF]. This model also included variables that represented the forage components (e.g. micronekton biomass) to allow for a more comprehensive assessment of the potential environmental changes that may arise from global warming [START_REF] Loukos | Potential changes in skipjack tuna (Katsuwonus pelamis) habitat from a global warming scenario: modeling approach and preliminary results[END_REF] Another modeling approach, habitat base standardization (statHBS), allows users to derive indices of relative abundance from CPUE data. This approach includes multiple component models that are capable of accommodating factors such as habitat, sampling method, and animal behavior [START_REF] Maunder | Interpreting catch per unit effort data to asses the status of individual stocks and communities[END_REF].

Additional approaches used to describe the environmental conditions associated with tuna fishing grounds include the nonparametric probabilistic environmental niche model (NPPEN) which has been applied to describe the environmental niche of the Atlantic bluefin tuna [START_REF] Fromentin | Oceanographic changes and exploitation drive the spatio-temporal dynamics of Atlantic bluefin tuna (Thunnus thynnus)[END_REF]. [START_REF] Reygondeau | Biogeography of tuna and billfish communities[END_REF]) used hierarchical clusters and nonparametric probabilistic tests to define environmental and biogeographic characteristic of tuna and billfish habitats worldwide.

More recently, researchers have begun to use the relatively new Boosted Regression Tree (BRT) method in ecology studies [START_REF] Martínez-Rincón | Comparative performance of generalized additive models and boosted regression trees for statistical modeling of incidental catch of wahoo (Acanthocybium solandri) in the Mexican tuna purse-seine fishery[END_REF][START_REF] Nieto | How important are coastal fronts to albacore tuna (Thunnus alalunga) habitat in the Northeast Pacific Ocean? Progress in Oceanography[END_REF] This powerful method combines two modeling approaches: regression trees and the boosting model. In this study, we used the BRT approach to better understand the role of the environmental variables in defining the preferred habitats of three tropical tuna species. We applied this model to the study areas of the Indian ocean (IO) and the Eastern Pacific ocean (EPO) both previously described in Chapter 2.

Fishing grounds environmental description

A general description of the environmental conditions found in the fishing areas studied, through the analysis of the histograms of twelve variables selected for both basins, the Indian Ocean (IO) and the Easter Pacific Ocean (EPO) is taken into account (see appendix C.). The distribution of each variable is described for each fishing mode (i.e., if the set corresponds to free schools (FS), Logs (LOG) or fishing aggregating device (FAD)), the species and the fish size range. All three species as considered for the IO and only YFT for the Eastern EPO, because of limited availability of data for the two other species. We also compared the environmental variables with a suite of partial randomly distributed locations (hereafter "random positions"), generated as explained before, to ensure we removed any environmental effects generated by mesoscale related processes. These random positions correspond to a spatial random redistribution of the data within a 250 km radius from the real catch locations.

Boosted Regression Tree (BRT) model approach

The BRT modeling approach is an ensemble method that combines the strength of regression trees models (which relates a response to their predictors by recursive binary splits) and the boosting model, which is an adaptive method for combining many simple models to give an improved predictive performance. This combined approach make the BRT different from more conventional techniques that aim to fit a single parsimonious model. The final BRT model can be understood as an additive regression model in which the individual terms are simple trees fitted in an advanced stepwise manner [START_REF] Elith | A working guide to boosted regression trees[END_REF].

BRT is a powerful method that can handle different kinds of predictor variables and accommodate missing data in those variables by using surrogates splits [START_REF] Breiman | Classification and Regression Trees[END_REF][START_REF] Nieto | How important are coastal fronts to albacore tuna (Thunnus alalunga) habitat in the Northeast Pacific Ocean? Progress in Oceanography[END_REF]). The model does not require prior data transformation or the elimination of outliers, it can fit complex nonlinear relationships, and finally, it can automatically handle interaction effects between predictor variables. Although they are a complex model, they can be summarized to provide powerful ecological insights and their predictive performance is superior to most traditional modeling methods [START_REF] Elith | A working guide to boosted regression trees[END_REF].

Similarly to GLMs, BRT models can be fitted to a variety of responses types (e.g., Gaussian, Poisson, binomial, etc.). Some of the most relevant features of BRT are briefly discussed below:

• The improvements in predictive performance are due to stochasticity and the model's random or probabilistic component. This is achieved by only using a random subset of data to fit each new tree which reduces the variance of the final model [START_REF] Friedman | Stochastic gradient boosting[END_REF]. Consequently, unless a random seed is set initially, the final models produced for each run will be subtly different.

• The sequential model-fitting process adopted by the BRT model builds on trees fitted previously and serves to increasingly focus on the hardest observations to predict.

• The learning rate (lr), or shrinkage parameter determines the contribution of each tree to the growing model while the tree complexity (tc) parameter controls whether the interactions are fitted: collectively these two parameters determine the number of trees required for an optimal prediction.

• The bag fraction (bg) parameter controls model stochasticity, specifying the proportion of data to be selected at each step. For example, at its assigned default value (0.5), 50% of the data will be drawn at random at each iteration, without replacement from the full training set.

In an ecological context, [START_REF] De'ath | Boosted trees for ecological modeling and prediction[END_REF] suggests that boosted tree and aggregated boosted tree models offer both opportunities and challenges for ecological analyses due to their quality as predictors and the illustration of the relationships between predictors and the response. While traditional regression methods produce a single "best" model, BRT models are capable of combining large numbers of relatively simple tree models in an adaptive manner to optimize predictive performance (e.g., [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF][START_REF] Leathwick | Dispersal, disturbance and the contrasting biogeographies of New Zealand's diadromous and non-diadromous fish species[END_REF]. The variables investigated (table 4.1) along with the environmental preferences of the three tuna species, described in chapters 1 and 3, give us preliminary information about what to expect in terms of tuna distribution. The specific characteristics of an open ocean as the EPO with a regular yearly current system compared to a closed basin as the IO with a complex current dynamics (Fig 2.3,2.4), can be some of the reason why the wider ranges of values are in the EPO for most of the variables. For example, lower sea surface temperatures and higher chlorophyll richness and more offshore positions (compared to the IO), could induce tuna associations at longer distances from thermal fronts. Deeper MLD and consequently a deeper thermocline could also influence the catchability of species as BET, which is less abundant mainly in free school and LOG sets (Fig 4.5,4.12).

Preliminary environmental comparison

On the other hand and knowing that tropical tunas avoid oxygen rich environments, the range of dissolved oxygen in both oceans seems to offer the conditions for a wide distribution of tuna. This apply for species as yellowfin and skipjack tuna that frequently remain on top of the thermocline and for bigeye specially in some areas even within the minimum oxygen layer, in the EPO (Fig. 2.12), levels that this species can tolerate. In the case of the wind speed this variable could equally impact the distribution of tuna in both basins. Even though, we will have to consider additional fishery variables, as the vessel capacity to relate with, due to the wind could affect the dynamic of the fishing net when a set is made.

Regarding the bathymetry, their role can be understood as a proxy of the influence of deeper water masses in a given area. This water could modify the oxygen levels and its distribution in the water column as well as the nutrient availability. In terms of mesoscale eddies, due to the higher percentage of all type of sets (free school and FADs) in the IO (Fig. 4.7-4.9), and to the fact that only FADs sets in the EPO are made in the equatorial region , we can expect longer distance to eddies due to their absence in the equator. As explained in chapter 2, eddies are present north of 5°N and south of 5°S (Fig. 4.19), for this reason shorter distances are expected mainly for free school sets in the EPO. This chapter describes the method developed to detect and classify thermal fronts. This is a paper published in Remote Sensing of the Environment (See Appendix H).

Introduction

Fronts are constitutive elements of almost all spatial structures observed at the ocean surface worldwide. These boundaries are equally as important in characterizing the epipelagic environment as continuous surface descriptors, such as temperature, salinity and ocean color. Fronts are primarily driven by physical displacements of surface waters; thus, sea surface temperature (SST) is by far the parameter by which fronts are most often detected. Synoptic satellite observations enable fronts to be identified at regional or even basin scale, according to data processing capabilities.

There are two primary methods by which fronts are detected: the gradient-based approach and the histogrambased approach. The Canny operator [START_REF] Canny | A Computational Approach to Edge Detection[END_REF] is the most commonly used gradient-based method. In general, this method applies an upper gradient threshold to identify a pixel as an edge and a lower threshold to discard it. If the pixel gradients are between both thresholds, only the pixels that is closest to the upper threshold are marked as an edge (i.e., skeletonization). The histogram-based approach detects the limit that divides two distinct pixel populations. The most commonly used method for this approach is the single edge detection algorithm (SIED) developed by [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF] that is based on a bimodal histogram of two water masses.

The SIED is developed in two main axes: the identification and correction of clouds and the edge detection itself. Prior to the detections, this method requires a standard preliminary smoothing of the images (generally using 1 km SST data), consisting of a 3×3 median filter in order to reduce the local noise. The detection process includes a division of the image into fixed windows of size 32×32 pixels, in which the algorithm searches for fronts. The algorithm examines the spatial properties of the SST field in each window to investigate the presence of a thermal limit between two water masses. Specifically, a SST histogram is computed from each window and tested for significant bimodality to determine if a frontal edge is present. Three internal parameters are defined by the SIED to formally identify a front: 1) the spatial cohesion threshold, θ = 0.90, to test the bimodality, 2) the signal-to-noise ratio, S = 4, related to a maximum error probability and 3) the population threshold, Pwi ≥ 0.25, that represents the minimum size ratio between water populations. The last stage of the analysis, termed the "following algorithm", joins contours that are slightly separated [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF].

Since 1992, many studies have developed upon the original Cayula and Cornillon method. In 1995, Cayula and Cornillon themselves applied their previous SIED algorithm to a sequence of SST images to develop the multi-image edge detector (MIED) method that simultaneously detects weaker fronts and improves the elimination of false detections. Ullman and Cornillon (2000) evaluated different gradient and histogram-based edge detection algorithms using Advanced Very High Resolution Radiometer SST data and compared their results with SST fronts obtained from in situ data. They tested false front detections and failures to detect fronts and concluded that the false front error rates were less important for the SIED than for gradient-based method. They suggested that SIED frontal detection algorithm can be useful in providing accurate statistics of front occurrence at scales > 10 km, but that gradient-based methods were more accurate at scales < 10 km. [START_REF] Ullman | Continental shelf surface thermal fronts in winter off the northeast US coast[END_REF] then applied the MIED algorithm to 12 years of SST images, revealing the presence of persistent fronts off the northeast US coast. [START_REF] Diehl | Geographic Window Sizes Applied to Remote Sensing Sea Surface Temperature Front Detection[END_REF] investigated an approach using "geographic window sizes" (window size is determined by the correlation of the data surrounding the window's central point) to avoid the limitation of the unique window size used by the SIED algorithm. They found that front detection is improved where fronts are smaller or more dense, mostly in coastal regions, but at a cost of a complex data re-composition.

In terms of expanding the SIED to other data types, Miller (2004Miller ( , 2009a) ) was among the first to apply the SIED to Sea-viewing Wide Field-of-view Sensor data to detect chlorophyll-a (Chla) fronts and boundaries of suspended matter. He combined these with SST fronts to describe the physical and biological interactions involved in coastal areas under tidal influence. Using Chla data, [START_REF] Wall | Satellite remote sensing of surface oceanic fronts in coastal waters off westcentral Florida[END_REF] applied a gradient-based and a histogram-based algorithm on the coastal waters off Florida, combining 32×32 and 16×16 pixel detection windows and modifying some SIED parameters. They found that the gradient-based algorithm was better at identifying near-shore Chla fronts than weaker offshore fronts.

More recently, [START_REF] Nieto | Mesoscale frontal structures in the Canary Upwelling System: New front and filament detection algorithms applied to spatial and temporal patterns[END_REF] proposed an improved implementation of the [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF] algorithm termed the combination of multiple windows (CMW), initially applied to 1 km SST data. This method, used in the present study, applies grids of frontal detection (four 32×32 pixel windows) that overlap by half their size in order to overcome the edge effect of the original SIED algorithm, whose detection efficiency decreases towards the edges of the windows. This method provides huge improvements from the standard Cayula and Cornillon SIED approach in terms of both edge detection (140%) and front length (30%).

Prior to the detection of fronts, a pre-processing of the data based on smoothing filters is needed in order to remove the noise introduced by the sensor and the uncorrected atmospheric effects. The smoothing procedure helps to preserve valid information from the original noise (the high frequency signal in the spatial domain) by improving the quality of the subsequent frontal detection. At the same time, the selection of an adequate window size is critical for the performance of the detection. All methods based on SIED have been almost exclusively applied to 1 km data (and mostly SST data) that facilitates the tuning of the algorithm and supplies the most detailed and accurate results. They generally use similar preliminary smoothing methods (a median filter with a 3×3 kernel) and the 32×32 pixel window. Table 5.1 summarizes the data resolution, preliminary smoothing and internal parameters used by several authors in the application of the SIED method. The only study known to us that uses a different smoothing method is that by [START_REF] Belkin | An algorithm for oceanic front detection in chlorophyll and SST satellite imagery[END_REF]. This study applied a median filter that considers a small window (3×3 pixels) within a larger one (5×5 pixels) before the detection process applied to both SST and Chla data.

Table 5.1: Parameters applied in previous studies using the [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF] SIED algorithm to detect sea surface temperature and chlorophyll-a fronts from satellite images.

The objective of our study is to define an adequate pre-processing procedure to detect fronts using 4 km data without losing relevant information (e.g., general patterns, detection of weak fronts, coherence of detections, and length). The considerable advantage of such upscaling is the ability to process data at the basin or global scale, minimizing processing time and avoiding data handling constraints. Thus, we extensively test frontal detections made with different combinations of preliminary smoothing parameters, including median, average, and Gaussian filters at four different kernel sizes (i.e., 3×3, 5×5, 7×7, and 9×9 pixels) and using different detection window sizes (16×16, 24×24, and 32×32 pixels). We aim to propose a new conditional smoothing method that maximizes edge detection quality from 4 km data.

We then perform a classification of the fronts at the basin scale, based on the intensity of their thermal gradient. The resulting patterns are described in particular for coastal and offshore regions, highlighting some oceanographic processes.

It is important to note that while we do not validate our frontal detections with in-situ measurements, we test the performance of the contextual smoothing method using 4 km data and consider all fronts that are detected to be real.

Methods.

Satellite data.

Daily 1 km and 4 km SST fields are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Aqua platform, for the period between 2002 and 2011 (http://oceancolor.gsfc.nasa.gov/). Another data set of 2 km resolution is sampled from 1 km data in order to analyze the variability of the frontal gradients according to different spatial resolutions (i.e., 1, 2 and 4 km). The quality flags available for 4 km (i.e., 0, 1 ,2) are tested to evaluate their effect on the detection of frontal structures. Flag 0 gathers initial detectability tests that are considered as a minimal requirement for pixels without cloud cover. Since flags 1 and 2 include a threshold that masks the highest SST gradients along with cloud borders, only flag 0 is kept.

Study areas.

Several regions around the world are used to test the effect of applying different smoothing parameters prior to the detections of fronts using the CMW method [START_REF] Nieto | Mesoscale frontal structures in the Canary Upwelling System: New front and filament detection algorithms applied to spatial and temporal patterns[END_REF] on 4 km data. Though the base algorithm for CMW, the SIED method, is known to have low sensitivity to cloud cover [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF], we select three areas of low cloud coverage to give a maximal spatial continuity in frontal detections. This allows us to measure the length of fronts that are detected, without spatial constraints. To test the effects of the preliminary smoothing methods, five clear images of each of the three areas (for a total of 15 days) are selected across ten years of data (about 10 7 pixels in total) in order to achieve statistically significant results. The resulting smoothing parameters are then applied and fronts are detected using the CMW method at the basin scale.

The study areas are each characterized by high mesoscale variability and include the region offshore of Morocco, the Mozambique Channel, and offshore of north-western Australia (Fig. 5.1). The Moroccan region, located between a rich coastal upwelling and offshore stratified oligotrophic waters, is influenced by the Canary Current. The variable intensity of the upwelling is related to numerous coastal topographic irregularities which make mesoscale structures (and hence, fronts) very common in this region [START_REF] Nieto | Mesoscale frontal structures in the Canary Upwelling System: New front and filament detection algorithms applied to spatial and temporal patterns[END_REF]Pacheco & Hernandez-Guerra, 1999). The Mozambique Channel is influenced by the North Equatorial Madagascar Current that contributes to the southward Mozambique Channel flow and to the high eddy activity found in this region. The southern part of the channel is affected by the local upwelling of southern Madagascar and in Delagoa Bight (26-28°S) (Lutjeharms, 2006). The north-western Australian region is impacted by several currents, including the Indonesian Throughflow (ITF), the Halloway Current (HC) and the Leeuwin Current (LC) that act together to generates permanent high intensity coastal fronts (Fig. 5.1)

Frontal detection and assessment of preliminary data smoothing

The default data smoothing generally applied to the data prior to the frontal detection consists of a simple 3×3 median filter (as in [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF], see Table 1). Nevertheless, preliminary tests (not shown) indicate that the performance of the frontal detection greatly depends on the type and intensity of the smoothing applied, independent of the internal settings of the SIED algorithm.

In this study, we evaluate the practical effects of smoothing on frontal detection performance, including the influence of the local gradient whose intensity is directly related to the ability to detect fronts. We propose here to use the local gradient as an intrinsic property of the fronts in order to separate them into "weak" and "strong" categories. To do this we first apply a 3×3 Gaussian filter to reduce local noise and then determine the minimum significant surface gradient in our data, as measured by the Sobel operator (Gonzalez & Woods, 2007). Considering the effective radiometric resolution of the SST data (0.15°C) and the maximum size of a pixel in an equidistant cylindrical projection at the equator (4.5 km), the weakest (bi-directional) Sobel gradient (as measured linearly in a 3×3 pixel matrix) is close to 0.017°C km -1 . Since residual uncorrected atmospheric artifacts tend to increase the measured gradient, we consider that 0.02°C km -1 is an adequate threshold to define significant SST gradients. We also confirm by visual expertise that gradients <0.02°C km -1 are generally associated with the background noise of the data and do not reveal interpretable oceanic structures.

Ultimately, we divide the fronts into two populations, separated by the mode of their distribution, thereafter labeled "weak" and "strong" fronts. The gradient associated with each frontal pixel is defined as the highest gradient value found at a maximum distance of three pixels from the front. This is done to account for the frequent slight spatial mismatch observed between the front position and its associated gradient. In order to define a representative threshold value for each type of fronts, the mode of the distribution is computed from a very large data set (in our case, one full year of daily data for the whole Indian Ocean, i.e., about 10 9 pixels). The median gradient value found is 0.042 °C km -1 . This value is then used as a reference for all regions of this study.

We then test the effects of different smoothing methods, or convolutions, using common filters (i.e., Gaussian, median and average) at four kernel sizes (3×3, 5×5, 7×7 and 9×9 pixels) on 4 km SST images prior to the frontal detection. All tests are performed for three different window sizes (16×16, 24×24, 32×32 pixels, hereafter named W16, W24 and W32). Windows sizes smaller than 12×12 pixels were not been tested because of the difficulty of the SIED algorithm finding a statistically valid solution for the separation of two water masses that allows a front to be defined. The tests are also performed without any convolution. A total of 39 window, filter and kernel combinations are evaluated.

The Gaussian filter is used to blur images and partly remove noise. When working with images, it is necessary to use the two-dimensional Gaussian function that is the product of two one-dimensional Gaussian functions (in both x and y directions). The median filter, widely used due to its ability to remove noise while preserving edges, works by computing the median of the neighborhood values. Finally, the average or mean filter reduces the variation between neighboring pixels. The constraints of using this filter includes an excessive influence of outliers pixels on the average, and a blurring effect of the filter in cases of high contrast.

Different smoothing combinations are applied to the 15 test images and fronts are then detected using the CMW method. In order to maximize detections, we adapt the CMW method to 4 km data by altering the internal parameters to θ = 0.65, S = 3.0 and Pwi = 0.10. In particular, a low value of Pwi improves the detection of frontal structures closer to the coast. It is important to note that after several tests on 4 km global area coverage data (not shown), we did not apply the "following algorithm" of [START_REF] Cayula | Edge Detection Algorithm for SST Images[END_REF] included in the original CMW method, as it did not show a visible improvement of frontal detections at 4 km. Contrary to the original SIED algorithm, no minimum front length has been defined because the CMW method already combines partially detected fronts.

The performance of the frontal detection is evaluated independently for the weak and strong fronts and for each combination of filter, kernel and window size. The performance assessment procedure included a statistical analysis and an expert-based approach. The statistical analysis consisted of the evaluation of: 1) the detection efficiency, defined as the total number of frontal pixels found in each image, and 2) the average length of the fronts (in km), a more complex parameter to define due to potential false breaks between fronts. A "reference" combination (i.e., the smoothing method most frequently used in histogram-based frontal detection studies) is defined as a 3×3 median filter combined with a W32 and is used to evaluate the improvement in the detection efficiency. Here, we examine front length by first, removing very short fronts (< 10 pixels; considered suspect) from the 15 test images, and then averaging the length of all fronts that have been detected. Next, an expert-based visual examination of the images is performed to account for indicators that are difficult to quantify, such as the shape of the fronts, the proportion of short fronts, the presence of possible "double fronts" and the proximity of fronts to the coastline.

Additionally the histograms of Sobel gradient values associated with fronts are computed for the Moroccan area. This is done to show the effects of the data resolution and the window size on the gradient distribution and to visualize the thresholds used to define the weak and strong fronts. We compare 1) different resolutions (1, 2 and 4 km) using the same window size (W16) and 2) the size of the detection window (16×16, 24×24 and 32×32) at 4 km resolution.

Performance of the frontal detections

In order to estimate the overall performance of the detections at 4 km, the spatial correspondence between the fronts detected and the gradient is quantified separately for weak and strong fronts. The "representative surface" of each front is first computed by considering a distance of three pixels from all frontal pixels. This is done to account for the precision of the front positioning found to vary from one to three pixels from the nearest maximum corresponding gradient. A detection rate is then calculated from the five clear images of both Moroccan and north-western Australian areas, given as the percentage of fronts that are detected and correspond to either a strong or weak frontal gradient.

Application of optimal smoothing at the basin scale

To test the optimal smoothing combination found in this study, we apply it at the basin scale in the Indian Ocean on 10 years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) of daily 4 km MODIS data. Thermal fronts are detected and monthly frequency of occurrences (in percentage) are mapped and divided into "weak" and "strong" front categories for 1) the north-east (NE) monsoon (December to March) and 2) the south-west (SW) monsoon (June to September). Both monsoon seasons are associated with specific regimes of winds and currents. Biogeographical regions as defined by Longhurst ( 2007) are superimposed to facilitate visual comparisons of the patterns of frontal occurrence.

The Indian Ocean is known to have specific oceanographic characteristics that differ considerably from the Atlantic and Pacific Oceans, mainly because is bounded in the north by the Asian continent. The thermal contrast between land and sea, due to the presence of the continent, creates a seasonal wind reversal and deep seasonal variability in the ocean currents [START_REF] Fieux | Current system in the Indian ocean[END_REF], making this an ideal ocean to investigate frontal detections.

Results and discussion

Effects of the preliminary smoothing

The results of the preliminary smoothing of the data summarized the combined effects of the filters at different kernel sizes and the effects associated with the size of the detection window. The effects of the internal parameters of the SIED algorithm optimized for 4 km data are minor compared to those of the smoothing type (not shown).

Due to the sensitivity of these factors to the front intensity, or thermal surface gradient, the results are presented here for both "weak" and "strong" front categories.

Frontal detection efficiency

The detection efficiency shows that the impact of the smoothing is considerable. The frontal detections performed without any preliminary convolution (Fig. 5.2) was much lower than those with a convolution, with very similar results among window sizes (Fig. 5.2a, b). The higher improvement found in the detection efficiency of the weakest fronts thanks to the smoothing is due to the fact that they are more affected by the spatial noise of the data when using the SIED algorithm than strong fronts. We find that window size shows the biggest effect on frontal detection efficiency, followed by kernel size and filter type.

The effect of the window size is found to be the most important factor for detection efficiency. The average increase compared to the "reference" smoothing (i.e., W32, 3×3 median filter) and the highest detection efficiencies obtained in this study is 71% for strong fronts (using W16, 5×5 median filter) and 120% for weak fronts (using a W16, 7×7 median filter) (Fig. 5.2a, b). The detection efficiency for weak fronts increased moderately between W32 and W24 (24%) and more strongly between W24 and W16 (68%) (Fig. 5.2a). The detection efficiency for the strong fronts, increased by 9% from W32 to W24 and by 10% from W24 to W16. Overall, the smallest detection window (W16) gives the highest performance in terms of detection efficiency for both weak and strong front intensities, regardless of the smoothing combinations. The detections made at W16 clearly show the advantage of this unusually small window size, without detection of spurious short fronts as might have been expected. The ability to detect spatially complex fronts, as well as coastal fronts, at this window size, is clearly enhanced relative to W32.

The second dominant factor, after the window size, is the spatial scale of the convolution, i.e., the kernel size. Its effect on detection efficiency is substantial for all window sizes (Fig. 5.2a, b) and especially pronounced for the smallest window (W16). In general, the effect of the kernel size, is visible for the weakest fronts up to the 7×7 pixel kernel (Fig. 5.2a) whereas a maximum detection is reached at the 5×5 pixel kernel for the strongest fronts (Fig. 5.2b).

The three types of filters tested showed different effects on detection efficiency. The Gaussian filter showed a relatively poor performance at W16 except with very large kernel sizes (i.e., 7×7 and 9×9 pixels). These kernels lead to inappropriate detections, such as the presence of double fronts (Fig. 5.2e, f, right panels), because of their insufficient smoothing efficiency compared to other filters. At W16, the median filter showed maximum detection efficiencies for kernels 3×3 and 5×5 for strong fronts and 7×7 for weak fronts, decreasing in efficiency thereafter (Table 5.2). The average filter showed very similar results for W16. Contrary to other filters, the average filter's efficiency increased for higher kernels and window sizes (Fig. 5.2a). Despite the similar performances of median and average filters, in general, the median filter outperforms the average filter and is hereafter selected as the optimal filter. Visual assessments are consistent with quantitative results (e.g., Fig. 5.2c-f). The visual improvement using the 5×5 kernel size is obvious for all fronts whereas the 7×7 kernel size slightly enhances the results for the weakest fronts.

Kernel sizes equal to or greater than 7×7 significantly degrade the results for the strong fronts (not shown) with a visible change of their shape. The best detection quality is consequently observed for a smoothing combination using median filter with the 5×5 pixels for strong fronts and 7×7 pixels for weak fronts. We find realistic spatial complexity and remarkably good frontal continuity without spurious double fronts with this combination at W16. This demonstrates the high stability of the CMW detection algorithm despite the decrease by a factor of four in the number of pixels analyzed at the window level, compared to the "reference" smoothing for an equivalent 1 km resolution image (see Fig. 5.4).

Frontal Length

Considerable differences in average front lengths for the 15 test images were found for all window and kernel sizes combinations (Fig. 5.3a, b). When no convolution was used, front lengths were minimal and the effect of the window size was negligible for both weak and strong fronts. In general, t he average lengths were mostly influenced by the large amount of relatively short fronts, which were more numerous with the use of small window sizes.

For weak fronts, front lengths for W24 were similar to W32, but in the absence of smoothing shorter fronts were observed with W24. Distinct local maxima appeared at the 5×5 kernel size for weak fronts and the 3×3 kernel for strong fronts. The influence of the kernel size was even more important for W16, with the highest values for the median and average filters found with the 5x5 kernel for weak fronts and 7×7 kernel for strong fronts (Fig. 5.3a, b). Front length substantially increased (23%) from W32 to W16, with maximum lengths generally detected for the different convolution using the 7×7 kernel at W16. This is similar to the window and kernel size combinations that find that maximum detection efficiency. On average, fronts associated with strong gradients were 9% longer than those associated with weak gradients. For strong fronts, the average front lengths were very similar across window and kernel sizes .

We find that the smallest detection window (W16) substantially increases the length of the weakest fronts (at both 5×5 and 7×7 kernels) (Fig. 5.3a). This is observed despite the fact that the geographical size of W16 (72×72 km for a 4.5 km pixel size) theoretically does not allow us to detect fronts longer than 100-150 km. The fact that the average length of the detected fronts is far higher (220 km) and relatively stable between the different window sizes, gives a high degree of confidence in the smoothing method presented, beyond that of the spatial scale of detection. This result is a possible consequence of the CMW procedure that combines four simultaneous detections grids and converges towards stable values of front length, despite the use of different detection scales.

The visual assessment is very helpful to discriminate results that are quantitatively similar. This is particularly the case between the median and average filter types, whose results are very similar in terms of detection efficiency and average front length. Those fronts detected with the average filter appear spatially more complex (Fig. 5.2c-f rightmost column). These fronts also show a much higher frequency of double fronts that do not correspond to real patterns in the data. The median convolution is not affected by this tendency and can consequently be visually confirmed as the most adequate filter. The data correspond to the average of five clear images for each of the three study areas, Morocco, Mozambique Channel and north-western Australia (i.e., totaling 15 images). (a-b) The total number of frontal pixels for the three detection window sizes, 32×32 (left column), 24×24 (middle column) and 16×16 pixels (right column) and the three filters, Gaussian (blue dashed line), median (red line) and average (green dashed line) applied at four kernel sizes, 3×3, 5×5, 7×7 and 9×9 pixels. Data for images where no smoothing was performed are labeled "no conv." (black dots). The "reference" or standard smoothing (3×3 median W32), generally used in front detection, is represented by black squares. The red and green squares show the best quantitative and visual results for both weak and strong fronts, obtained with median 7×7 and 5×5 respectively. The images show front detections for north-western Australia on November 24 th , 2009 for (c) 3×3, (d) 5×5, (e) 7×7 and (f) 9×9 pixel kernel sizes for the the median filter at window sizes of W32 (left column), W24 (middle left column) and W16 (middle right column) and the average filter at the W16 window size (right column). Table 5.2. Summary of sea surface temperature and frontal occurrence spatial and seasonal patterns and their variability for the Aden gulf/ Arabian sea, Bay of Bengal and Eastern South Africa regions (see figure 7 for corresponding twodimensional fields).

Global performance of 4 km to 1 km frontal detections

We find that W16 offers the best performance for 4 km due to its high detection power, the stability in front length for kernel sizes > 3×3 pixels and the spatial coherence from the visual assessment. This is found for both front intensities (weak and strong), especially when W16 is associated with median or average filter. We therefore suggest that the optimal smoothing method for preprocessing images uses a median filter with a 7×7 kernel for weak fronts and a 5×5 kernel for strong fronts at W16 (Fig. 2a,b). The detections obtained with this optimal smoothing combination are visually similar to the detections found from independent 1 km resolution images processed with the "reference" combination (Fig. 5.4a,b). Since the shape of the fronts at the near-pixel level change with the spatial scale, its comparison with the frontal detection efficiency of the 4 km data is very difficult. Nevertheless, it is clear that an important quantity of fronts are very well detected on the 4 km data relative to the 1 km data, indicating that the most relevant fronts were detected. Additionally, despite the slightly greater uncertainty in their location induced by the smoothing effect, the spatial distribution of fronts is coherent with 1 km data. Only a minor number of fronts are not detected with 4 km data because of their spatial proximity. This limitation is clearly due to the width of the detection window, despite the fact that we defined a procedure at the lower window size limit permitted by the SIED algorithm. Similar to that observed in the present analysis, [START_REF] Nieblas | Front variability and surface ocean features of the presumed southern bluefin tuna spawning grounds in the tropical southeast Indian Ocean[END_REF] found that the CMW algorithm applied to 4 km data missed some fronts relative to 1 km data and that the average frontal length is greater for 4km data than that obtained from 1 km data.

Effects of window size

To illustrate the effect of window size on frontal detections, we computed the average frequency of frontal occurrence to daily (2003-2012) 4 km MODIS SST data offshore of Morocco, applying the optimal preprocessing smoothing of a median filter with 5×5 and 7×7 kernels found above and using the window sizes W16 and W32. Results displayed for February (characterized by moderate coastal upwelling (Barton et al., 1998)) indicate that detections improved from large to small window sizes (based on visual assessments; Fig. 5.5a, b) mostly in the inner part of the wide continental shelf (Fig. 5.5c, d), characterized by a previously-defined secondary upwelling front (Makaoui et al., 2005). We also found a homogeneous increase in the frontal occurrence measured by a 120% increase for weak fronts and a 20% increase for strong fronts. In particular, only the smallest window size W16 (Fig. 5.5b, d) allowed spatially close fronts to be correctly separated, between themselves as well as from the coastline.

Regardless of the window size used (i.e., W16 or W32), coastal patterns of high frontal occurrences were associated with persistent upwelling fronts, mostly originating from the shelf at the vicinity of capes Ghir, Jubi and Bojador and visible with ocean color data (Pacheco & Hernandez-Guerra, 1999). During upwelling events in this region, surface waters are advected away from the coast and generate intense fronts between surface and subsurface layers (Pelegrí et al., 2005). This is especially true south of Cape Ghir where upwelling fronts and filaments are observed far from the coast. Fronts are very concentrated over the continental slope in winter because of the quasi-permanent seasonal thermocline and the slightly lower intensity of the upwelling. (c-d)The black rectangles highlight the areas where substantial improvements in front detection using the 16x16 window size were observed over the shelf in a complex coastal environment. The 200 m isobath is superimposed in the zoom frames (bottom) .

Effects of the spatial resolution and window size

The Sobel gradient of the SST, which represents one of the most objective evidences of frontal presence, is used to estimate the relative performance of the frontal detections at different spatial resolutions and across window sizes. As previously mentioned, all frontal pixels are by definition associated with a gradient > 0.02°C km -1 , in order to reduce the risk of false detection.

For the Moroccan area, approximately 68% of the frontal pixels correspond to gradients above this threshold, which means that 32% of the pixels belong to fronts that do not match the elementary criteria of a front, not even at the weakest possible intensity. This is due to the fact that the SIED algorithm follows a fixed SST threshold value, which does not necessarily correspond to pixels at the same position as the maximum gradient associated with the front. Therefore, it is interesting to compare the distributions of the SST gradient according to spatial considerations, i.e., data resolution and the size of the detection window. This show that frontal gradients are linearly dependent on the spatial resolution of the data (Fig. 5.6a), as the minimal spatial resolution of the data (1 km) is far greater than the spatial scale of the in situ oceanic fronts. When the gradient distributions are normalized relative to 4 km data, they are very similar in terms of shape (Fig. 5.6b). In this case, the histogram of the 4 km data only shows slightly more frontal pixels detected relative to 1 and 2 km data for the gradients associated with the strongest fronts and slightly less for the weakest fronts. The gradient distribution at 4 km resolution shows even weaker differences between the different window sizes (Fig. 5.6c) that are not likely significant.

Front-gradient validation

Since all images are selected according to their low cloud coverage, all spatially structured gradients are supposed to be real and therefore associated with fronts. Consequently, these gradients (Fig. 5.7a, b) are used to validate fronts and estimate potentially missing detections and the influence of the window size for both weak and strong fronts.

The detection rates, expressed in percentage, are computed for the Moroccan and the north-western Australian areas (Fig. 5.7c-n). Detections are similar for both areas when using W16, i.e., 89% (Morocco; Fig. 5.7e) and 88% (north-western Australia; Fig. 5.7k) for weak fronts and 93% (Morocco; Fig. 5.7h) and 91% (north-western Australia; Fig. 5.7n) for strong fronts These detection rates are 49% and 25% higher for weak and strong fronts, respectively, than those obtained with W32.

The overall proportion of fronts that were not detected dropped from 34% (W32) to 10% (W16): a more than a three-fold decrease. Detection rates between areas and between weak and strong fronts were more similar when using W16 as compared to the larger window sizes, in particular W32. These results indicate that the increase in detection efficiency previously obtained by using W16 corresponds to a validated improvement relative other window sizes. They also confirm the relevance of the optimal preliminary smoothing of the data. 5.4 Large scale application: example of seasonal patterns of frontal occurrence in the Indian Ocean

Weak fronts

During the NE monsoon, a good correspondence is observed between the occurrence of weak fronts and low winds (Fig. 5.8a and c). Numerous weak intensity fronts with occurrences > 3% are observed from western India to the Arabian Sea during the NE monsoon (Fig. 5.8c), with the highest occurrences close to the coast. The presence of such fronts in this region may be explained by several factors, such as 1) the effect of the circulation south of Sri Lanka in the exchange of water between the Bay of Bengal and the Arabian Sea (Reppin et al., 1999), 2) a branch of the westward North Monsoon Current that carries low salinity water from the Bay of Bengal and flows along the west coast of India [START_REF] Vinayachandran | The warm pool in the Indian Ocean[END_REF] or 3) the presence of the prevailing north-east trade winds that bring cool, dry continental air to sea, which intensifies the evaporation and leads the surface cooling (Madhupratap et al., 1996).

Front occurrences > 3% are also present around Australia, mainly in the northeastern and southern areas, due to the effect of different currents (e.g., the Indonesian Throughflow, the East Gyral Current, and the Leeuwin Current) in creating boundary fronts in these areas (Fig. 5.8a, b, c).

During the SW monsoon, weak fronts are far less frequent (< 2%) than during the NE monsoon, especially in the northern Indian Ocean basin (Fig. 5.8a, b, c right frame). Front occurrences > 3% are only present in the Mozambique Channel, Indonesia and northern Australia. Numerous mesoscale eddies and high eddy kinetic energy in the Mozambique Channel [START_REF] De Ruijter | Observations of the flow in the Mozambique Channel[END_REF]Donguy & Piton, 1991;Tew Kai et al., 2009) probably contribute to the high front occurrences observed there. In the eastern basin, the seas around Indonesia and northern Australia are influenced by the South Java Current (SJC), which contributes a northwestward flow during the maximum flow period of the Indonesian Throughflow, which occurs during SW monson [START_REF] Schott | The monsoon circulation of the Indian Ocean[END_REF]) (Fig. 5.8 b,c).

Strong fronts

Strong fronts during the NE monsoon with frequencies > 5% (Fig. 5.8d, left frame) occur in the northern Red Sea, in the coastal area of the Bay of Bengal, in the South China Sea, off southern Madagascar and in the region of the Agulhas Return Current along the Subtropical Convergence Zone. In the northern Bay of Bengal, the activity of the East India Coastal Current and the presence of cyclonic gyres in the southwest part of the bay contribute to front occurrences via coastal upwelling [START_REF] Vinayachandran | Phytoplankton bloom in the Bay of Bengal during the northeast monsoon and its intensification by cyclones[END_REF].

In southern Madagascar, the front frequency possibly reflects the impact of the South Equatorial Monsoon Current (SEMC) and the presence of a local upwelling [START_REF] De Ruijter | Observations of the flow in the Mozambique Channel[END_REF] caused by the westward bend in the East Madagascar Current. Among all fronts investigated in the southern Indian Ocean by Lutjeharms and Valentine (1984), the Agulhas Front is described as having the steepest gradient, associated with a very consistent temperature of 18.4°C. In western Australia, the strong Leeuwin Current, partially supplied by subtropical waters, flows southward and follows the coast around Cape Leeuwin at the southwestern tip of Australia, and beyond 120°E, generating quasi-permanent high intensity fronts. This feature is probably reinforced by the equatorward winds off Western Australia that oppose to the Leewin Current, which is strongest during the NE monsoon [START_REF] Schott | The monsoon circulation of the Indian Ocean[END_REF][START_REF] Schott | Indian Ocean circulation and climate variability[END_REF]. Finally, the Subtropical Convergence Zone, between 40°S and 45°S, is characterized by permanent meander fronts, occurring at progressively at higher latitudes east of 80°E.

During the SW monsoon (Fig. 5.8d, right frame), strong fronts show patterns similar to those found during the NE monsoon, except that fronts of the northern Bengal almost disappear and strong coastal fronts are observed off Somalia, the Aden Gulf and the western Arabian Sea. The Somalian region is impacted by the atmospheric Finlater Jet, which originates from the east African coast [START_REF] Fieux | Current system in the Indian ocean[END_REF] and helps generate the strong Somali upwelling along with the divergence created by the Southern Gyre (Fig. 5.8b).

In summary, as expected and regardless of season, strong, high intensity fronts frequently occur in coastal regions and in semi-enclosed seas (i.e., the Red Sea and the Persian Gulf). A few regions show weak intensity coastal fronts, i.e., the Arabian Sea and western and southern India during the NE monsoon; and north-western Australia, southern Indonesia and the Mozambique Channel during the SW monsoon. The offshore areas are principally dominated by weak fronts.

Conclusions

The improvement in the detection of fronts using 4 km data demonstrates the importance of the preliminary spatial smoothing proposed here. The tests performed independently for the two gradient intervals (i.e., weak and strong fronts) confirm that the best results are those obtained by the specific combinations of the following parameters: a median filter, kernel sizes of 5×5 pixels for strong fronts and 7×7 pixels for weak fronts, and a detection window size of 16×16 pixels.

We show that this preliminary smoothing method can be applied to 4 km data at the regional or basin scale levels with comparable results to those obtained using 1 km data. Low resolution data (i.e., 4 km) strongly lightens the constraints related to data manipulation and computing time. We clearly show that major weak and strong fronts are correctly detected and that the frontal continuity is preserved despite the small size of the detection window. A comparable quality of detections is obtained despite the use of sixteen times less data, relative to 1 km resolution images, even if it is clear that 1 km resolution data will always supply more detailed and accurate results.

We observe that strong fronts are mostly found in coastal regions and weak fronts are mostly found in the open ocean. This suggests that the consideration of the frontal intensity may help to spatially differentiate distinct mechanisms of frontogenesis. Over continental shelves, especially in nearshore areas, the methodology proposed here allows us to make a very detailed description of the link between fronts and various physical processes, such as coastal and offshore currents and coastal upwelling.

We selected areas with relatively low cloud cover in order to test the effect of the combinations of parameters of our method on estimating the length and spatial continuity of fronts without constraint or bias due to cloud cover. Otherwise, the Cayula & Cornillon algorithm is not affected by cloud cover, contrary to gradient-based methods. This methodology can be applied in areas with various conditions of cloudiness, including highly clouded regions, such as the Peruvian or Californian coasts, provided that adequate cloud corrections have been previously applied.

Since thermal fronts constitute one of the most important mesoscale features in the ocean, their role in modulating biological productivity [START_REF] Bainbridge | The Size, Shape and Density of Marine Phytoplankton Concentrations[END_REF]Olson & Backus, 1985;Strass, 1992) as well as their direct influence on animal behavior [START_REF] Laurs | Albacore tuna catch distributions relative to environmental features observed from satellites[END_REF]Pakhomov et al., 1994;Polovina et al., 2000;[START_REF] Palacios | Oceanographic characteristics of biological hot spots in the North Pacific: a remote sensing perspective[END_REF] is a growing area of interest. The application of this method is potentially useful to better understand, at an ecological level, the association of different organisms with different front intensities as those described here.

modeling scheme

In this study, we created a modeling scheme, based on a series of boosted regression tree (BRT) models that were constructed and fitted using the 'dismo' package (Robert J. Hijmans, Steven Phillips, John Leathwick and Jane Elith) and the 'gbm' package (Greg Ridgeway) in the R program (version 3.1.1). The first section of the scheme, named "catch models", included all the models which used log transformed catch data as the response variable. The second section of the scheme, named "random catch models", grouped all the models which used the predictors or environmental variables derived from the random positions. The third section of the scheme, named "dominance models", included the binomial analyses based on the dominance of each species caught on FADs (Fig 6.1).

The modeling process consider four groups of predictor : general or classic variables, mesoscale variables, fisheries variables, and temporal variables (Table 6.1). Pearson's correlation of the predictors was checked prior to the construction of the scheme (i.e., Table 6.2 ).

Data sets, for each model, identified as having an "important" quantity of information (i.e., n >1000) were then fitted using a training data set (60% of the total data) and a test set (40% of the total data). This step was undertaken to test the overall performance of the BRT model. Where data sets were insufficient (i.e., n<1000), a cross validation fitting method was used (see [START_REF] Elith | A working guide to boosted regression trees[END_REF]Table 6.3). Catch models (green area), random models (gray area) and dominance models (yellow area) show the model ID (1 to 16), the size of the species (small, large and the unique size for SKJ) the fishing mode related (FS or FADs) and the total percentage of explanation. For BRT general models (1 and 2) the percentage of contribution of the more important predictor, size, is shown.

Results and Discussion

In order to facilitate the presentation of the results and their discussion, a preliminary analysis based on the average relative contribution of the predictor variables in the BRT scheme was made. About six or seven predictor variables were found with contributions > 6%, average contribution, (Table 6.4, 6.5) then we decided to retain the first six most important predictors of each model for discussion.

General Models

The "general" BRT models (Model 1: YFT, Model 2: SKJ, and Model 3: BET) include all variables presented in Table 6.1 as well as the fish size. These models explained 72%, 74%, and 47% of the total variance observed for these three species, respectively.

General model for YFT

For YFT (model 1, 72% of explanation), fish size (44%), fishing mode (10%), and year (7%) were the three explanatory variables that contributed the most with the total variance (Fig. 6.2). High YFT catch values were positively related to large fish size and to FS (fishing mode). Low catch values were especially observed between 2005 and 2008. Conversely, a regular catch was associated with a wider SST range (24°C -32°C) than reporter by [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF]. who found found high frequencies of YFT catches related to areas with a SST ranged between 28°C-30°C .Our observations can be supported by the capacity of YFT to forage beyond their normal temperature range [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF] , although this species usually prefer warmer waters [START_REF] Stretta | Forecasting models for tuna fishery with aerospatial remote sensing[END_REF] and generally beyond 25°C (Arrizabalaga et al. 2015). Distance to Anticylonic eddies (Dist A. Eddies) and the Eddy Kinetic Energy (EKE) contribute the least with 4.9% and 4% respectively.

General model for SKJ

For SKJ (model 2, 74% of explanation), year was the most important predictor of the catch, contributing to 19.5% of the variance observed. Dist C. Eddies (13.6%) and EKE (8.7%) were second and third highest contributors (Figure 6.3). Significant inter-annual variability was observed, mostly between 2006 and 2007, with a strong reduction of the catch by set. On the other hand, a positive relationship was observed between catch and Dist. C. Eddies (starting at 50 km). Furthermore, low values of EKE were observed to be related to an increased catch volumes. A similar pattern was also observed for SKJ in the North Pacific [START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF]. (Zainuddin et al. 2004b) found that high values of EKE was not specifically linked with SKJ catch in the equatorial Atlantic.

At the same time, a positive effect of the shallow mixed layer depth (between 20 m and 50 m) was observed probably derived from the effect of the wind speed velocities (>2 m/s, <4 m/s) on the stratification of the water column. Arrizabalaga et al (2015) reported the preference of tropical tunas for stratified waters where mixed layer depths < 80 m.

General model for BET

Model explanation (model 3, 47%) is lower that for the two other species. As for YFT, fish size contributed the most (19.4%), followed by year (8.9%) and SST (8.7%) (Fig. 6.4). Larger individuals were noted to be especially dominant. Opposite to YFT, the fishing mode did not appear to have a significant effect here. Furthermore, catch values were positively related to SST ~ 25°C and SST > 28°C. [START_REF] Lee | Spatial and Temporal Distribution Patterns of Bigeye Tuna (Thunnus obesus) in the Indian Ocean[END_REF]) reported a range of surface temperature between 27 and 29°C as the preferences of BET in the Indian Ocean. Regarding to mesoscale variables, catch was negatively related to short distances from anticyclonic (Dist. A. Eddies) and cyclonic eddies (Dist C. Eddies).

Unsurprisingly, the relationships observed in these three general models for the mesoscale variables (i.e., EKE, Dist Fronts, Dist Eddies) are complex and overall, we were unable to interpret them correctly. This complexity is due to the "effort" of BRT method to fit different relationships associated with completely different parameters, notably fishing mode and fish size. For example, possible possible relations with anticyclonic or cyclonic eddies are presented in the model outputs as either very weak and flat (i.e., as seen for YFT) or seeming important but meaningless for models 2: SKJ and 3: BET. This leads BRT to evaluate the role of the predictors separately (i.e., by size in their respective fishing modes) as we proposed in the following step of the modeling scheme where we study the environmental conditions related to the catch of each of the three species. High percentage of explanation is shown for small individuals by models 4 and 5 for sYFT (74%) and for sBET (84%) (Fig. 6.5). For both species, strong positive relationship are observed between catch and short distances to anticyclonic eddies (Dist. A. Eddies) with 27% and 18% of contribution respectively. The same relationship is found between sBET and distance to cyclonic eddies (Dist. C. Eddies: 7%). These observations suggest the proximity of the fishing sets (schools) to eddy structures (Fig. 6.8).

Furthermore, predictor variable Year displays an important variability for both species (sYFT: 14% and sBET: 16%) while variable season highlights a well defined pattern for sYFT catch reaching a high level during the inter-monsoon season (Fig. 6.6, 6.7).

Regarding to the continuous variables, SST and Bathy for both species are well related with catches. Tunas are found in the temperature range 28°C -30°C. These findings agree with the outcomes of [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] for yellowfin tuna, as mentioned in the general models section. Models 8 and 9 explain about 60% and 75% of the total variance for lYFT and lBET respectively (Fig. 6.9. Contrary to small-sized individuals, large tunas catches increase at distances >200 km to anticyclonic eddies (lYFT: 17% and lBET: 11%) (Fig. 6.10, 6.11, 6.12).

For lBET, another mesoscale predictor, the eddy kinetic energy (EKE) contributes the most to the variance of model 9 (22%). Catches are associated with low EKE values and long distance to anticyclonic (>400 m) and cyclonic eddies (>200 m). This indicates that tunas could be attracted by subsequent processes generated by these mesoscale structures leading to high catches relatively far from the eddies borders. For this species another type of structure appeared among the first contributors: the distance to weak fronts (Dist W. Fronts: 6%). This may be due to their higher frequency of occurrence, more offshore than strong fronts [START_REF] Roa-Pascuali | Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application[END_REF]. At the same time, shallow mixed layer depth (11%) (i.e., <40 m) confirms that in tropical regions lYFT is most often found in the upper mixed layer (Schaefer et al. 2007). On the other hand, the effect of variable Season on tuna catches is observed during the second intermediate-monsoon (Int.II) and the north-eastern monsoon (NE-Monsoon).

Compared with juveniles tunas (small-sized tunas), a negative relationship is observed between large tunas catches and warmer waters (i.e., >27°C). Even though some studies have reported that yellowfin tuna species is more abundant in waters between 20 -30°C they also state that it can be present in temperatures between 18-31°C [START_REF] Blackburn | Conditions related to upwelling which determine distribution of tropical tunas off western Baja California[END_REF]. Ortega-Garcia (1998) reported the distribution of yellowfin in a temperature range of 17 -31°C. Regarding bigeye tuna, it is well known that due lager individuals can dive and remain in deeper waters thanks to its physiological conditions. An optimum range of temperature seems to be 17°-22°C that coincide with the range of temperature of the permanent thermocline (ISSF 2015). Model 6 and 7 explain 42% and 28% of the overall variance respectively(Figure 6.13), a lower percentage than for free school models. Nevertheless, as for free school models, the mesoscale predictors contributed the most to the total variance. A significant association is found between catches of sBET and short distances to cyclonic eddies (11%) while for sYFT a wider range of distances is observed (13%). Longer distance can be related to the own characteristics of the drifting of FADs which, due to the influence of the currents,cover large areas. For example, FADs are moved far from the coastal eddies by the equatorial current (Dohan, 2013, pers. comm., 23 September). This aligns with the preliminary examination of the single variable Dist. A. Eddies by species and for both fishing mode (see appendix C.). These histograms show that sets made on FADs tend to be at larger distances than the free school sets, which are close to the eddy borders.

Furthermore, for both species, a good correspondence is found between catches and distance to anticyclonic eddies (11%), as shown in Fig. 6.14 and 6.15). In the case of bigeye, with a 8% contribution, catches are related with depths between 3000 and 4000 meters (Fig. 6.16).

For both species, SST is positively related with catches at temperatures above 26 °C, similarly to FS. For MLD, catches were associated with stratified waters between 20 m -50 m. [START_REF] Schaefer | Simultaneous behavior of skipjack (Katsuwonus pelamis), bigeye (Thunnus obsesus), and yellowfin (T. albacares) tunas, within large multi-species aggregations associated with drifting fish aggregating devices (FADs) in the equatorial eastern Pacific Ocean[END_REF] have reported that tropical tunas are predominately found above the thermocline when associated with FADs. Compared to the contribution for the small-sized individuals, higher percentages of explanation are obtained from models assessing large-sized tunas (lYFT: 57%, lBET: 55%). These results show differences in the environmental conditions between small and large tunas (i.e., juveniles and adults), suggesting that the effect of the environment for tunas caught on FADs is more important when they are adults (Fig. 6.17).

In regard to mesoscale predictor Dist A. Eddies (11%) show a negative association of lYFT catch with short distances, as well as with low EKE values.

lYFT catches are present in areas with deeper mixed layer (> 50 m). This predictor came in second place, behind year, and explain 11% of the overall variance. We also noted that catches for lYFT were characterized by a similar SST range to those caught on FS and small sized individuals caught on FADs (i.e., 26°C -30°C). Predictor Dist A. Eddies (relative percentage = 11%) show a negative association of lYFT catch with short distances, as well as with low EKE values. Finally with a contribution of 8% predictor Bathy showed a positive effect on catch at depth >3500 m, probably related with the depth of the continental slope as reported by [START_REF] Teo | Oceanographic preferences of Atlantic bluefin tuna, Thunnus thynnus, on their Gulf of Mexico breeding grounds[END_REF] in the case of the bluefin tuna (Fig. 6.18).

For lBET, SST was the top predictor, explaining 16.4% of overall variance, but in contrast with lYFT, catches of lBET were associated with the lowest observed SST range (21°C -25°C), typical temperatures over the limit of the MLD (10-20°C) [START_REF] Howell | Spatiotemporal variability in bigeye tuna (Thunnus obesus) dive behavior in the central North Pacific Ocean[END_REF]).These catches were also associated with a MLD < 30 m in agreement with [START_REF] Schaefer | Simultaneous behavior of skipjack (Katsuwonus pelamis), bigeye (Thunnus obsesus), and yellowfin (T. albacares) tunas, within large multi-species aggregations associated with drifting fish aggregating devices (FADs) in the equatorial eastern Pacific Ocean[END_REF]

VARIABLES

On the other hand, it was important to note that distances to either eddy type did not appear as the top contributors, contrarily to the fisheries predictor Vess. year that showed a contribution of 8.3% of the variance. This variable showed a continuous increase in the level of lBET catches from 1990 (Fig. 6.19). For SKJ caught on FADs (model 12) the predictors explained 42% of the total variance (Fig. 6.20). As in most models, Year contributes the most (17%) followed by MLD (11.7%), SSC (8.7%), SST(8.6) , Dist A. Eddies (8.3%), Dist C. Eddies (8.2%). As for lYFT and lBET, a positive relationship was observed between MLD (< 50 m) and SST above 26°C. This observation supports the findings of [START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF] who reported that an increase of SKJ CPUE beyond 26°C and with Andrade & Garcia (1999) who found that higher CPUE for this species in waters with SST between 22-26.5°C. Surprisingly, SSC appeared for the first time among the top contributors of all catch models evaluated, with a positive effect noted on catch values when concentrations were > 0.2 mg.m -3 (Fig. 6.21, 6.22, 6.23). [START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF] reported a positive effect on SKJ tuna CPUE in a range of 0.22 -0.27 mg.m -3 . Finally, a positive effect on catch was observed for short Dist A. Eddies contrarily to the observed for Dist C. Eddies where a positive effect was associated with long distances.

Dominance models by fishing mode and size

The dominance models aim to test the environmental influence on the relative dominance between species. As detailed earlier in this chapter, this dominance is computed as the relative weight of each species in each of the single set used.

The relative dominance within the three species are summarized in Table 6.6. The relative dominance is very high for Free Schools (FS) with 59% of sets containing only 1 species and only 7% containing 3 species. This percentage is completely different on FADs (well known to aggregate all tropical tuna species, including by-catch species) with 91% of sets with 3 species and only 5% containing 1 species. For this last reason, the dominance models are only tested for FADs associated catches for which the relative dominance is really meaningful. Dominance model 13: sYFT and 14: sBET constructed for small individuals on FADs, explain both 36% of the total variance. These models were almost exactly symmetrical models, with similar proportions of variance explained by the same variables, the contribution of each one being exactly inverse along its range of variability. This is because YFT and BET are the only ones for which a "small" size fraction is accounted, their respective location in space is consequently mutually exclusive.

The first predictor, Year, is followed by SST for both species with a contribution in relative variance of 12.4% and 13.1% for sYFT and sBET respectively. A negative relationship was found between SST ranged between 28°C and 30 °C and sYFT dominance. For sBET the opposite patterns were observed, as well as for the distance to eddy borders. While sYFT was dominant at Dist A. Eddies > 200 km and show a wide range for Dist C. Eddies, BET was dominant close to anticyclonic eddies and at long distance of cyclonic eddies (Fig. 6.25). Dominance for large YFT on FADs, model: 15 (38% of the total variance) was highly related to bathymetry (12.4%), with a wide range of depths (1000 m -5000 m). This is likely due to the drifting effect of FADs offshore and their deployment in different areas near and far to the coast. The high wind velocities observed (> 7 m/s) can strongly influence the trajectories of FADs. Distances to anticyclonic eddies had a positive effect on large YFT dominance at a distance of 200 km (Fig. 6.26).

The dominance BRT model for SKJ (model:16) explains 46% of the total variance. This is similar to the equivalent "catch" model for the same species (42%) (Fig.

Conclusions of the modeling approach

A preliminary model (not shown) has been constructed using the total catch of the three species together, YFT, SKJ and BET, considering all variables by groups as: general variables (SST, SSC, SSC dynm, Wind speed, MLD, Bathy, Dissolv. Oxygen), mesoscale variables (EKE, Dist. W fronts, Dist. S. Fronts, Dist. A. Eddies, Dist. C. Eddies), fisheries variables (Vess. year, Vess. capacity) and time variables (year, season) as well as fish size. This model (explained a low percentage of the total variance (32%) of the catches but more importantly, showed complex relationships that are furthermore very difficult to interpret in terms of underlying processes. Consequently, we decided to construct first three separate models by species (general models 1:YFT, 2:SKJ, 3:BET), still including: species, size and fishing mode.

These models highlighted the relevance of fish size as a central predictor, at a more important level than the fishing mode. Thus, the following modeling scheme was developed in order to consider species, fish size and fishing mode as a fundamental approach. We show first that all "catch" models (4 to 12) explained a greater percentage of the total variance than "Dominance" models (13 to 16). This means that the catch itself, is probably as relevant as abundance, here understood as dominance, to determine the environmental preferences of a determine habitat. Nevertheless, this can not be taken as a definitive conclusion as true absence are fundamentally impossible to estimate from catch data only.

Catch models

The first important point is that catch models showed significantly higher percentages of explanation for YFT caught on FS than on FADs (74% vs 60% for small YFT and 60% versus 57% for large ones) as well as for BET (84% vs 28% for small individuals and 75% vs 55% for large ones) .

These differences between fishing modes clearly suggest that the environmental influence is much more important for tunas on Free Schools than tunas associated with FADs. This is a double consequence that the natural environmental preferences of tropical tunas are particularly high (60, 75 and 84% of explanation) and that this behavior is as least perturbed by the FADs, notwithstanding the fact that the time spent by tunas under FADs is largely unknown. This is well explained by the own physical dynamics of FADs, highly dependent on the ocean currents, that make them drift over larger areas showing wider ranges of values for each of the parameters evaluated. This was clearly show in the analyses of the histograms presented in section 1.

The second very important result comes from the simulation with the "random positions" models (Model 4.1:28%, Model6.1:3%, Model 8.1: 3%, Model 10.1: 7%) created for both size categories of YFT caught on FS. This shows that the amount of variance explained by these models dramatically decreases for both small and large YFT. This suggests that catch per set is strongly dominated by the environment at the mesoscale level, i.e. at distances less than 200 km.

A third important result, that we mostly observed for the Indian Ocean, is the relative high contribution of the 5 mesoscale variables, at a similar or higher level than for the eight general variables tested (Table 6.5, yellow cells). This important contribution of the mesoscale variables is by far due to the spatial links with both types of mesoscale eddies, as measured by the distance to the catch to their border.

In 83% of the BRT catch models constructed, at least one of the mesoscale predictors Dist A. Eddies or Dist C. Eddies appeared among the 3 first top predictor variables. In 58% of the cases, both predictors were simultaneously present among the 6 first variables.

As already noted, fish size is more important than differences between species. This is particularly the case for YFT and BET for both fishing modes where the distance to eddies (anticyclonic or cyclonic) for small-sized individuals show a strong positive relationship with catches for distances < 200km. For largesize individuals, the positive effect is related with longer distances, as summarized in the following conceptual model (see Figure 6.27). We also observed that variable, year, contributed the most to the variability of the catch level in all models, with an average percentage of contribution of 13.3%. and important interannual variability. This means that either the influence of climatic variables (not found) or the fishing strategy may explain this variability.

The conceptual model on figure 6.27 has to be considered in three dimensions. This structure probably explain the exceptional importance of mesoscale eddies and the fact that they are generally poorly correlated with SST. At the contrary, their surface signature is much stronger in terms of resulting surface productivity, as often observed in the Mozambique channel. This is particularly relevant for some species and especially for small individuals caught on free schools (FS) with 26% of the observed variability for small YFT and 17.9% for small BET associated with anticyclonic eddies.

The global importance of SST in the catch models is relatively high with an average contribution of 9.3%. Nevertheless, only FS catch models showed positive effects of the temperature for juveniles of YFT and BET at the range between 26 °C and 30°C. Relationships with SST for large individuals are much more complex to interpret and show sometimes negative or erratic relationships, for example if the attractive effect for tunas is due to productive processes linked to better foraging conditions. For example, tunas are strongly associated to the productive effect of eddies that are sometimes associated to low SST.

The Eddy kinetic Energy (EKE) is a similarly important contributor (8.9% on average), with strong differences between species. EKE, that notably characterize the intensity of the currents around eddies is specially important to consider in relationship to the distance to eddies, still according the spatial conceptual model presented above.

The importance of EKE is mostly associated to both small and large BET caught on FS: high catches of juveniles shows a tendency to be close to eddies at distances <200 km , in association to high levels of EKE (a characteristic of eddies close to their border), whereas the opposite is observed for large individuals whose catches are higher at a larger distance to eddies borders (> 200km) and for low levels of EKE. The importance of EKE is generally less pronounced for FADs associated catches, at various distances to eddies, as shown for large YFT. Again, the main exception for FADs associated catches is observed for the BET, whose EKE explains 12.4% of the catch variability. As previously noticed, this is probably related to the fact that FADs are often drifted in the immediate border of eddies, associated to high EKE conditions, and that BET are maybe more attracted by the three-dimentional structure of eddies, because of their deeper habitat.

Conclusions from the random models

As previously mentioned, "Random positions" models (4.1, 6.1, 8.1 and 10.1), specially built to simulate environmental conditions at the vicinity of the real positions of the catches, in the unique perspective to test the mesoscale influence, clearly show a drastic decrease of all model explanations, falling to a percentage of explanation of 28%, 3%, 6% and 5%. For the first model (small YFT from FS original positions), only Year, bathymetry and SST persist to explain a significant part of the catch variability.

In this respect, the construction of such a simulation is extremely successful in showing the extremely high importance of the mesoscale in tropical tunas distribution.

Conclusions from the dominance models

Dominance models on FADs (YFT: 36%, BET: 36%, SKJ: 46%) showed on average similar relatively low levels of explanation compared to the "catch" models, with respectively of 42%, 57% and 28% of the total variance. This means that except for BET, the importance of the "fishing habitat" is not fundamentally different if described from the simple presence of schools than from the level of the catches, related to the school size. Dominance model on FADs show that the main predictor were similar a those found for their equivalent catch models: Year, SST, EKE, Dist to C/A eddies and MLD.

Contrary to the catch models, predictors Distance to Eddies were specific for small sizes as follow:

• For small-sized YFT Dist A. Eddies have a positive effect in dominance when distance were > 200km and Dist C. Eddies positive effect when distance where between 0 -600 km

• For large-size YFT positive effect in dominance when Dist. C. Eddies was = 200 km

• For small-sized BET Dist A. Eddies have a positive effect in dominance when distance were < 200km and Dist C. Eddies negative effect when distance where between 0 -600 km

The influence of predictor Dis. S. Fronts was observed for SKJ where short Dist. S; Fronts was positively related with Dominance of SKJ.

General conclusions

On average, 7 predictors explain between 13% and 8% of the catch variability (respectively Year, Dist-A-Eddies, SST, EKE, MLD, Dist-C-Eddies and bathymetry) whereas the less important variables explain between 4.6% and 1.8% of the catch variability. For example, the season predictor show invariably low levels of explanation for catches (3.6% on average).

The most astonishing result is the exceptionally low contribution of the chlorophyll-a concentration (3.9%) despite the fact that exceptional high levels of catches of YFT have been reported in association to exceptional blooms in the Indian ocean (Fonteneau et al. 2006).

In the same line, despite special efforts to objectively quantify the temporal dynamic of the surface chlorophyll-a concentration (SSC_dynm), its contribution to the catch variability remain extremely low (1.8%), the lowest of all variable tested, about two times less than the SSC itself. This means that the local enrichment processes occurring near the sea surface are not significantly "attractive" for tunas, probably because of the large spatial and temporal lag that exist between this primary enrichment and a significant presence of their preys, occurring between one and two trophic levels beyond.

Similarly, and despite numerous qualitative observations of tunas associated with frontal structures described in the literature, the importance of fronts, even discriminated in term of intensity, is not as important (3.3% and 3.4%) as expected. Because these structures are moving and associated with various complex physical process (coastal currents, eddies, etc.) they are probably secondary compare with the importance of the mesoscale eddies.

It is also probable that more regional studies would show more accurate relationships in this respect. This is probably even more relevant for the Eastern Pacific, where highly contrasted systems are blended in a single model, that just can not summarize possible opposite contributions among variables. This is probably the case in the Indian ocean between the seasonal Somali upwelling and the warm equatorial Indian pool, where fundamentally different processes occur.

At the opposite, both cyclonic and anticyclonic eddies, that are typical long lasting structures (weeks or months) where virtually all the pelagic trophic chain may occur, are much more significant structures in term of nutrition interest for tunas than planktonic blooms or thermal fronts.

The distribution of each variable is described for each set type (i.e., if the set corresponds to free schools (FS), Logs (LOG) or fishing aggregating device (FAD)), by species caught and by fish sizes. Nethertheless, only YFT is considered, because of limited availability of data for the two other species. As for the IO, we compared the environmental variables with a suite of partial randomly distributed locations (hereafter "random positions"), generated as detailed in chapter 4, to ensure we removed any environmental effects imposed by mesoscale related processes. These random positions correspond to a spatial random redistribution of the data within a 250 km radius from the real catch locations.

modeling scheme for the Eastern Pacific Ocean (EPO)

The modeling scheme for the EPO was constructed in a similar way than for the IO, considering the results obtained in the general model (model 1) with the predictors: fish size and fishing mode included in the model. We first developed a scheme based on the fish size, despite of the similarities between the level of contributions of both variables (Fig. 7.2). The overall explanation of all models in found to be significantly less than in the IO, probably because of the diversity of the ecosystems concerned, mostly coastal and equatorial upwelling and gyres. Table 7.3: BRT parameters and fitting method used for the Eastern Pacific Ocean (IO) models. ID represent the model number of identification. The parameters include the tree complexity (tc), the learning rate (lr), the bag fraction (bg) and the number of trees (nt) predefined when cross validation method was used.

Table 7.4: Relative contribution of the predictors by model and category for the BRT modeling scheme General variables (blue), mesoscale variables (green) and temporal variables (dark yellow). Mean contributions are presented in orange. Note the higher average contributions of the general variables category for all models (column Total category (blue). Percentages per variable are relative to the total percentage of the model. Table 7.5: Relative contribution of the predictors by model and category for the BRT random and dominance models. General variables (blue), mesoscale variables (green) and temporal variables (dark yellow). Mean contributions are presented in orange. Note the higher average contributions of the general variables category for all models (column Total category (blue)).

Results and Discussion

As for the Indian Ocean, a preliminary analyses based on the average relative contribution of the predictor variables in the BRT scheme was made. Six predictor were found with average contributions > 7%.

Catch models for YFT

General Model

The BRT model 1 explained 41% of the total variance for YFT total catch on all fishing modes (FS, LOG and FADs) together. The predictor variables that contributed the most to the variance were (in relative values): SST (12%), fish size (11.6%) and F. mode (9.9%). Variables fish size and F.mode are equally important(Fig. 7.3). The fish size, show positive relationship for large-sized individuals. Among the three fishing modes evaluated, free school (FS) shows the most important association with the catch level. Due to the different dynamics observed in the sub-regions, it is difficult to interpret general variables as SST and Bathy as well as the mesoscale variables. As for the IO, it is necessary to analyze them separately by size and fishing mode.

Catch models by size and fishing mode

Catch models for the EPO showed percentages of explanation between 41% and 15%. Lower percentages correspond to models developed on FADs. Due to the low number of observations, the model for LOG has been set up only for medium-sized YFT. Model 2 for small-sized YFT on FS explained 37% of the total variance against 18% for model 3 for small individuals on FADs. On FS, the predictor with the highest relative contribution (26.5%) was the SST with a positive effect on catch for temperatures > 25°C. This predictor was follow by Dis C Eddies (11.5%) and Dist A. Eddies (10%), showing positive relationships at distance < 200 km. Meanwhile, SSC (7.2%) concentrations < 1 mg/m 3 were negatively related with catch and Disslv. oxygen with concentrations < 4.5 ml/L (Fig. 7.4 upper panel).

For model 3, despite the low percentage of explanation for the FAD-associated individuals (18%), the same patter that found for FS was observed here with an important contribution of predictor SST (15.6%). for temperatures > 26°C as well as for low SSC concentrations (< 0.2 mg/m 3 ) (Fig. 7.4 bottom panel). This observation agrees with the fishing grounds conditions preliminary described (see appendix D) where FADsassociated individuals were associated with low SSC levels (<2.5 mg/m 3 ) (Fig. 7.5).

Figure 7.6 hows how catches on FS are located close to the coast for higher SSC levels while FADs catches are mainly offshore,associated with low SSC concentrations. This clearly indicates that the natural drift of FADs, for example in the Equatorial Pacific also drift tuna toward warmer and lower SSC concentration waters. Compared with the cold and highly productive coastal waters, the Equatorial upwelling is characterized by warmer and less productive waters.

Catch models for Medium-sized YFT on FS

For medium size individuals on FS (model 4), the percentage of explanation was 40%, a similar explanation than for the small individuals. The highest contributors for this size were similar to those found for small sized individuals as SST (21.2%), associated with temperatures > 25°C. Positive relationships were observed with distances < 300 km for Dist. A. Eddies (18.9%) and < 200 km for Dist. C. Eddies (6.8%). Dist. S. Fronts presented a contribution of 7.6% (Fig. 7.7), significant at long distances from strong fronts (> 400 km). This indicates that this category of fronts, defined as strong by [START_REF] Roa-Pascuali | Detection of mesoscale thermal fronts from 4 km data using smoothing techniques: Gradient-based fronts classification and basin scale application[END_REF], and/or their associated biological processes may have an attractive effect on tunas even at long distances. This can be observed for specific areas in the EPO, as observed in the Costa Rica Dome, where permanent filaments derived from strong coastal fronts reach warmer waters favorable for tunas (Fig. 7.8).

Catch models for Medium-sized YFT on LOG

For medium sizes on LOG (model 5: 33% variance explained), variable SST continues to be the strongest contributor (16.3%), positively related with catches for temperatures < 25°C. This variable was followed by Dist. W. Fronts (13%)) and Dist. C. Eddies (12.8%) with positive effects at distances > 50 km < 200 km respectively. Shallow MLD (< 25 m) at fishing locations contributes with 9.7% as often found in the upper mixed layer depth in tropical regions (Schaefer et al. 2007). YFT catches were associated with bathymetries >4000 m (Fig. 7.9).

Catch models for Medium-sized YFT on FADs

Very low percentage of explanation was found for the model (20%), compared with small-sized on FADs, wind speed, MLD and SST contribute in a similar way with the total variance ~13%.

Catch model for Large-sized YFT on FS

For large-sized individuals model 7 explained 39% of the variance. Important results were obtained for predictors SST and SSC, high contributors with 17.4% and 11.5% respectively (Fig. 7.11). Comparing the conditions for these two variables when high levels of catch were obtained with the environmental description of the fishing grounds -see appendix D-(Fig. 7.12), a match was observed. This indicates that catches were made in areas that tend to characterize YFT environmental preferences in terms of warmer and oligotrophic waters. Arrizabalaga et al. (2015), reported surface temperatures beyond 25°C as a preference for YFT. On the other hand, [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF] found strong associations between SSC concentration (values ranging between 0.1 mg.m -3 -0.4 mg.m -3 ) and yellowfin tuna catches.

Catch model for Large-sized YFT on FADs

Model 8 explain 35% of the total variance for large-sized YFT catches on FADs (Fig. 7.13). As observed for catches on FS, variables SST and SSC contribute the most to the variance this time with 23.2% and 12.5% respectively. The same observation was made for FS is made here for FADs for these two variables. Results of the comparison with the fishing grounds environment characteristics showed that conditions imposed by FADs, mainly lower SSC concentrations, are associated with very low catches. Contrary, high catch levels can be due to sporadic drift of FADs close to the coast. (Fig. 7.14 and 7.15).

On the other hand, despite the high percentage of contribution Dist A. Eddies (8.9%) and Dist C. Eddies (6.8%), there was no strong association between distance to eddies and catches. This is mainly due to the drifting of the FADs in the Equator where there are no presence of eddies.

Random models for YFT

The BRT "random" models showed a lower percentage of explanation than their normal version (table 7.5). Overall, low percentages of total variance were observed for FADs (model 3.1:15%, model 6.1:15% and model 8.1: 26%), contrarily to LOG (model 5.1:30%) and FS (model 4.1:31% and model 7.1:33% ) with the exception of model 2.1 (24%).

Random models are similar to catch models. Medium-sized YFT catches on FS (model 4.1:31%) and LOG (model 5.1: 30%) constantly show the highest contributions for SST and mesoscale variables among the six firsts contributors (figure 7.16). This relative importance of the mesoscale also in the random models may be related to specific dynamics of the different regions of the EPO. This effect suggests that other analyses must be conducted by regions.

For catches on FS, a positive relationship was observed with Dist. C. Eddies (16.8% of relative contribution) for distances < 400 km, contrarily to Dist S Fronts (15.7%) and Dist W. Fronts, negatively related at distances < 200 Km. SST was positively correlated for low values (Fig. 7.16). Other predictors as Dissolv. oxygen (10.5%), EKE (9.4%) and Bathy (8.9) did not show a significant effect on the catch level.

For LOG-associated YFT, it is important to note the effect of both thermal front intensities among the first contributors, at long distances to strong fronts (Figure 7.16 lower part). Taking into account the LOG spatial distribution, close to coastal areas, this observation can be explained as an strategy of tunas to avoid very low temperatures still having an effect of enrichment by strong fronts. This can be confirmed by by the bathymetry (12.9%) between 0 and 3000 m, showing that YFT moves between the coast and the continental slope where fronts and the filaments derived from them can reach several kilometers offshore ( [START_REF] Nieto | Mesoscale frontal structures in the Canary Upwelling System: New front and filament detection algorithms applied to spatial and temporal patterns[END_REF].

Dominance models for YFT

As for catch and random models, Dominance models for EPO show very low percentages of explanation for FADs (model 10:12%, model 11:25%). At the contrary, FS (model 9) for medium-sized individuals showed one of the highest percentage of all models for EPO: 41%, very close to the previous FS model examined. Here, Bathy and SST were the variable that contributed the most to the total variance with 13.7% and 13.2 respectively. Significant catch levels were reached at depths < 2000m from the coast with temperatures < 24°C (SST ,13.2%). [START_REF] Block | Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range[END_REF] report for YFT individuals between 8 and 16kg, corresponding to this medium sizes category, with temperature preferences > 17.5°C. It is important to note that mesoscale variables Dist C Eddies, Dist A. Eddies and Dist S. Fronts showed almost identical contributions as well as the wind speed (~11%). Distances to eddies were related with important levels of catch at distances < 300 for anticyclonic and < 200 for cyclonic eddies while strong fronts had a positive effect at short distances <100 km. This indicates a strong attractive effect of the mesoscale for medium size individuals. Meanwhile wind velocities until 5m/s were associated with high catch levels. Strong relationship between wind speeds between 0.5 m/s -5.4 m/s, the number of fishing events and their respective catches for bigeye tuna was observed by [START_REF] Pereira | Behaviour of bigeye tuna in a baitboat fishery[END_REF]. It is also important to note the contributions of the SSC for this model (9.3%) (Fig. 7.17) where high pigment concentrations are related with high catch levels. Existing research suggests that high concentrations of SSC are needed to produce significant catches; however, the exact dynamics of this relationship can vary from one ecosystem to another as tuna adapt their foraging and survival strategies.

modeling general conclusion for yellowfin tuna in the the EPO

The general observations regarding the percentage of explanation in the modeling scheme, show lower percentages, for catch and dominance models compared with was found for the Indian Ocean. An important similarity is the lower explanation observed for FADs models against the higher percentages on LOG and FS.

Another important observation is the average of the relative contribution by category of variables. This show that the average contribution of the general variables were more important in the EPO than the mesoscale variables.

• Predictor SST was present in 99% of the models evaluated while SSC was present in 50% of the total models. Contrarily to the Indian ocean, here mesoscale variables distance to fronts frequently appear among the first contributors. • Variables as MLD, Bathy and Dissolv. oxygen where more frequently present in the EPO than in the Indian Ocean. • Variables Dist. A. Eddies and Dist. C. Eddies are present in 60% and 40% of the catch models respectively.

High contributions observed on variables as SST and Chla as well as the mesoscale variables can be a consequence of the general evaluation of an area with at least three well defined subregions, Equator, coastal area and coastal upwelling. Nevertheless strong relationships can be well described for each fishing mode. One of the considerations will be applied the modeling method in each region separately.

Chapter 8 8 General conclusions and Perspectives

This study provides an approach to the comprehension of the mesoscale role in the definition of the tropical tuna's environment, mainly based on thermal fronts (detection and classification in weak and strong fronts) and on eddies. Distances of tunas' catches to both mesoscale structures were computed. Additionally we considered different groups of variables as the classical, the temporal and the fisheries indicators (Fig. 8.1). We first described the ecological definition of habitat at the same time that we presented the studies made about the environmental preferences of tunas (Chapter 1). We continue this manuscript with the comparison between both study areas, the Indian Ocean (IO) and the Eastern Pacific Ocean (EPO), and with the description of their specific oceanographic characteristics (Chapter 2).

As stated in Chapter 3, three main species make part of the tropical tunas, mainly caught by the purse seiner's fisheries worldwide, 13% and 20% of the world total production for the Indian Ocean and the Eastern Pacific Ocean respectively. In this chapter we described the main characteristics of yellowfin tuna Thunnus albacares (Bonnaterre, 1788), skipjack tuna Katsuwonus pelamis (Linnaeus, 1758) and bigeye tuna Thunnus obesus (Lowe, 1839). We described the purse seine fishery and his high efficiency. In Chapter 4 we present the data and methods used to describe: 1) the fisheries data and the advantages in terms of accuracy to use single sets catch records. Therefore we described the method used to correct the species and size composition in both study areas. 2) the environmental variables including the construction of a set of randomly distributed data and the calculation of the sea surface chlorophyll dynamics based on its recent temporal history (i. e., days before catch). Tthe parcel of water where the catch took place, was defined as day zero (d0).

Then in Chapter 5, we presented the structuration of the Indian and Eastern Pacific ocean basins through an statistical and expert-base approach proposed for the quantification and classification of thermal fronts. We defined two different types of fronts to which tunas can be associated with, based on a partitioning of their gradient. After several tests in different regions we applied this method at basin scale. We improved the detection of fronts using 4 km data demonstrating the importance of the preliminary spatial smoothing and make front detection routinely available for ecological studies.

We also showed that this preliminary smoothing method can be applied to 4 km data at the regional or basin scale levels with comparable results to those obtained using 1 km data. We found that strong fronts are mostly found in coastal regions and weak fronts in the open ocean for both Oceans. This suggests that the frontal intensity (i.e weak and strong fronts) helps to spatially differentiate distinct mechanisms of frontogenesis acting as attraction systems for tropical tunas. Over continental shelves, especially in nearshore areas, the methodology proposed allowed us to make a very detailed description of the links between fronts and various physical processes, such as coastal and offshore currents and coastal upwelling. For the Eastern Pacific Ocean, we found that the weakest fronts are mostly associated with the equatorial upwelling and in the intermediate coastal regions of Central America and to the border of the tropical gyres whereas strong fronts are mostly associated to coastal upwelling regions.

Then, in Chapters 6 and 7 We analyzed the relationships between tuna catch level and tuna dominance with the different categories or group of variables, including the original mesoscale variables described in Chapters 4 and 5. We used the multivariate Boosted Regression Tree (BRT) analyses, a method recently applied in ecology studies, to construct a modeling scheme by fish size and fishing mode for each study area. These schemes consisted in three steps where catch, random positions and relative dominance were modeled

For the analyses we also used a preliminary description of the environmental conditions of the fishing grounds presented in the appendix C and D. Twelve environmental variables are described by fishing mode and fish size for yellowfin tuna (YFT), skipjack tuna (SKJ) and bigeye tuna (BET) in the Indian Ocean and only for yellowfin tuna (YFT) in the Pacific Ocean (See Appendix C and D). We found strong differences in the range of values for each variable between oceans (i.e. deeper MLD for the EPO). For example, the MLD in the EPO showed important differences among fishing modes (FS, LOG and FADs) and among fish-size. MLD show spatial differences among fishing grounds, whereas no differences were found at the mesoscale level.

Overall for both study areas BRT model was able to better explain the relationships explored for free school (FS) individuals compared to those explored on FAD-associated ones. The results found on FS are interpret in this study as the closer indicator of the real environmental preferences of tunas. Despite the sampling constraints of the own fishing activity, strong patterns where observed at fish size and fishing mode levels. We found an extraordinary and unexpected influence of the mesoscale in determining tuna's habitat confirmed by the poor results, in terms of percentage of explanation, obtained with all spatially randomised models. . As stated in chapter 6, the relative proximity to eddies is related with different levels of EKE and is highly characterized by the presence of small-sized individuals. Contrarily, large individuals are constantly found further from the eddies. Initially, this observations can be associated with an "attractive" effect of the eddy dynamics on juveniles maybe as a consequence of underlying physical and biological processes, linked to the maturity of water masses from the core of the eddies towards its surrounding, probably related to a size increase of their respective preys. For adults this effect is weaker, probably because their autonomy and less physiological constrains that let the to explore wider environmental conditions, sometimes far from eddies. To confirmed this statements and the implications of these observations it is important to go one step beyond of the measure of distances, choosing other indicators to identify processes that influence the tuna behavior related to eddies.

At the same time the low influence of mesoscale fronts, found in the models mainly observed for the Indian ocean, can be due to their interactions with mesoscale eddies that makes them under evaluated by the model. An example of these interactions can be the presence of strong fronts in the eddies borders. These assumptions could be confirmed through the use of new indicators which can take into account these interactions. These indicators could also be based in the different eddy phases, describing if they are decaying or increasing on intensity and size. These classification could be useful in order to have a better approach to the eddies and tuna interactions.

It is also important to mention that the important role of the mesoscale is found despite the equatorial location (5°N to 5°S) of many fishing grounds, 40% for FS and 70% for FADs in the Indian Ocean.

In the Eastern Pacific Ocean, we recognized the three well defined areas were usually tuna is distributed. We investigated three FS, LOG and FADs sets but not Dolphin-associated sets because of data access restrictions. Even though, we infered some common environmental conditions between all three areas for FS catches.

As for the Indian ocean, small sized individuals are strongly related with the proximity to mesoscale eddies close to the coast and in areas with strong eddy dynamics as the Costa Rica Dome. This region and the coastal area are characterized by the presence of medium-size individuals for which the mesoscale strong fronts become important along with the eddies effect. On the other hand exactly the same pattern was found for large-sized individuals as in the Indian ocean been located far from eddies while sharing similar regions than the small and medium-sized individuals. In summary mesoscale "effects" for all sizes occur in the same area but a different distance of the structures, behavior probably defined by their physiological capacities and the by the spatial distribution of their preys.

The strong environmental patterns of the different regions observed in the EPO (coastal upwelling, the equatorial tongue, and the warm pool to mentions some) suggest the possibility to make the same modeling approach but independently for each area.

In the case of FAD-associated individuals we found a variation of the environmental conditions identified even from the preliminary description of the fishing grounds. Important aspects are derived from our analyses concerning the environment of FADs. First of all the variability observed in the parameters evaluated in a defined region. For example, the FS and FADs fishing grounds in the Indian ocean share a quasi-homogeneous area in terms of SST and SSC concentrations; then the differences in these conditions are essentially related with the offshore drift of FADs.

In the EPO, even if mesoscale variables remain important, the most relevant ones are the classical variables. The SST and the SSC highly influence tuna distribution, characterized by a wider range of values for each of these parameter. In general FS individuals of all sizes were associated with high SSC close to the coast (in upwelling systems) or in the central north EPO. This observations coincide with the general description of the fishing grounds recognized for FS individuals.. SST and SSC were also important for FADs where individuals are drifted along the equator. Only few FADs remain close to the Peru upwelling system, later drifted by the Humboldt current towards the Equator. The observations conclude that despite the Equatorial upwelling the difference in SSC concentrations compared with the coast is important. FADs will not drift in "poor" waters but notably in waters with a much less SSC concentrations and higher temperatures. This was observed by the combinations of results obtained from the fishing grounds SSC and SST conditions and the model.

The proximity to mesoscale eddies has a low effect on FADs sets in the EPO compared with the Indian Ocean. This is probably due to their trajectory in or close to the equator, where the Coriolis effect is negligeable. The eddy effect is more visible beyond the 5°N or 5°S limit. To understand the dynamics of these conditions in time, and therefore its positive or negative influence on tunas, it would be very interesting to continue working with FADs trajectories. A preliminary analyses about the drift of FADs in rich or poor waters and simulation of the trajectories for both ocean have been made (see appendix E, F).

C. Environmental description of the fishing grounds in the Indian Ocean

C.1 Description of the fishing grounds

We present a general description of the environmental conditions found in the fishing areas studied, through the analysis of the histograms of twelve variables selected for both basins, the Indian Ocean (IO) and the Easter Pacific Ocean (EPO). The distribution of each variable is described for each fishing mode (i.e., if the set corresponds to free schools (FS), Logs (LOG) or fishing aggregating device (FAD)), the species caught and the fish size range. All three species as considered for the IO and only YFT for the Eastern EPO, because of limited availability of data for the two other species. We also compared the environmental variables with a suite of partial randomly distributed locations (hereafter "random positions"), generated as explained in chapter 4, to ensure we removed any environmental effects imposed by mesoscale related processes. These random positions correspond to a spatial random redistribution of the data within a 250 km radius from the real catch locations.

C.1.1 Environmental characteristics

In this section, we present a description of the environmental conditions found in each fishing ground, based on our analyses of the environmental data extracted at each single set catch position. This analyses will serve to provide a preliminary comparison and interpretation of the different preferences exhibited by the three tuna species between the two fishing modes (FS vs. FADs for the Indian Ocean). The outcomes of this analyses will also be useful to interpret some results of the multivariate analysis, particularly those relating to catch levels in relation to their environment.

In the following sub-sections, we present a brief description of the environmental distributions for each of the twelve variables considered in this study, by species and size category. It is important to remember that SKJ has a unique size due to its size is less than the small size range define for YFT and BET (see chapter 4). It is also important to note that these distributions will, in part, reflect fleet-related dynamics as well as true environmental preferences as we relied solely on fisheries data to determine fish positions.

C.2 Conclusion on the habitat shift observed on FADs

The use of the simple distribution densities of the environmental variables show that important differences in the "fishing habitat" of all three tuna species were observed between catches associated to fishing aggregating devices (FADs) and catches associated to free schools (FS).

For many general variables (SST, Wind speed, MLD, SSC) as well as for some of mesoscale parameters as the distances to both cyclonic and anticyclonic eddies, catches made on FADs indicates that these schools are clearly associated with a broader range of variability of all these variables. This is very likely attributed to a "dispersion" effect due to the physical drifting of the FADs, that correspond to a wider range of environmental variability.

Although very pronounced, the interpretation of the parameters related to the mesoscale structures is more complex:

The distances of the catches from all thermal fronts show a low apparent attractiveness of tunas caught on FS (no major differences observed between true catch locations and random positions), meaning that distances to fronts are not mesoscale dependent, up to distances of 250 km, which is the maximum range of these "random positions". This will be also reflected by a moderate importance of this variable in the statistical models explaining the catch level according environment.

At the contrary, the distance of the catches from eddies borders is very mesoscale dependent, at a range between 0 and 300 km. This is specially pronounced for the smallest tunas of all three species, who are closer to eddies than large individuals, as highlighted for small YFT and large BET (Fig. 6.14 bellow). This shows that the resulting effect of the attractiveness of schools caught on FS make them more distant to eddies borders (at a range of distances between 0 and 300 km), while this distance is very flat from 0 to 500km for schools caught on FADs.

The important point then is the comparison between these distances and those recorded from the deliberately "random locations" (dotted lines on Fig. 6.14), whose goal is only to test the effect of the mesoscale (0-250km). This effect is very noticeable for all species and both eddy types at various intensities but always very clear. It is for example moderate for both small YFT and large BET caught of FS, closely associated to anticyclonic eddies in both cases (Fig. 6.14 left, plain and dotted blue curves), at short distances (<100km) for the small YFT and at longer distances (mode at 300km) for large BET, without strong differences between true and random positions, which means that this effect is in fact quite close to "random".

The difference with the "random positions" is at the contrary very pronounced for schools caught on FADs (plain and dotted red curves), whose random positioning (red dotted curve)makes them virtually much closer to eddies, with a pronounced mode at a distance of less than 100 km. This means that in reality, FADs are probably drifted far away from eddies border, by purely physical processes.

D1. Environmental characteristics of the YFT fishing grounds in the Eastern Pacific Ocean

Sea surface temperature SST for the catches done on FADs and LOG are similar but important differences were found between FS and these two categories, probably because of habitat differences, with the FS catches done mostly in coastal areas (figure 6.1a). We observed similar SST values for small and medium sized individuals caught on FS, mainly between 20 °C and 25 °C. For large YFT a wider range of values toward warmer waters was observed. When we compare the true catch positions with the random positions, the distribution patterns are different for small and medium sizes. This shows that these two sizes actively select low temperature waters < 25°C within the local mesoscale range of SST. No differences were observed for individuals on FADs and LOGs .

Bathymetry

Bathymetric similarities were observed for individuals of all sizes caught on FS with two well defined ranges < 2000 m and between 2000 m -6000 m. This shows that medium and large individuals occupies deeper regions. Only large individuals are associated to deeper areas for FADs and LOG. FADsassociated and LOG-associated individuals for all sizes prefer depths > 2000 m. No significant differences were observed between true catch position and random position distribution patterns, except for continental margins (mostly for large YFT), probably because of the proximity of coastal areas for several fishing grounds.

Wind speed

A high wind speed variability is observed among the three fishing modes, progressively increasing from FS, LOG and FADs, probably because they correspond to different areas. Individuals caught on FS are associated with lower velocities (pics between 2.5 m/s -5 m/s) than LOG and FADs-associated individuals that showed intermediate (pics at 3.0 m/s -5 m/s) and high velocities (pics at 3.5 m/s -6 m/s) respectively. Only insignificant differences were observed between the random position and the real distribution patterns for all sizes and fishing modes.

Mixed Layer Depth

Strong differences were observed among all fishing modes and sizes. Narrow distributions were shown for the small, medium and large individuals on FS (i.e., ~ 50m) while wider distributions were observed for LOG with maximum depths at 75 m and FADs at 100 m, a proof that the three fishing grounds are spatially very different (Figure 7.1). Naturally no differences are observed with random locations because of the slow spatial variability of the MLD at the mesoscale level.

Sea surface chlorophyll

Individuals on FS showed a much wider SSC distribution for all size categories, specially for small and medium sized YFT, reaching maximum concentrations (= >1mg.m 3 ). Contrarily narrower ranges were observed for object associated individuals, whereas LOG-associated fishes showed concentrations values < 0.5 mg.m 3 , FADs showed the lowest concentration reach < 0.3 mg.m 3 among all fishing modes. There were almost no observed differences between the distribution patterns of the true catch positions and the random positions.

Sea surface chlorophyll dynamics

For all fish sizes, object-associated (LOG and FAD) individuals showed small values (-0.1 to 0.1), indicating no variation of SSC through time. At the opposite, a broad distribution was observed for FS individuals with a tendency toward positive values, mainly for medium-sized and large individuals. This indicates that fish sizes on FS prefer recently enriched surface waters, probably from coastal areas. No differences were noted between random patterns and true catch locations.

  Eastern Pacific Ocean..............250 D1. Environmental characteristics of the YFT fishing grounds in the Eastern Pacific Ocean.251 E. Preliminary work on FADs Trajectories................................................................................257 E1. FADs trajectories data........................................................................................................258 E2. Method...............................................................................................................................258 E.3 First approaches to the new FADs data base......................................................................260 E.4 Analyses derived from the work with the observers in the lab..........................................261 F. Simulation of FADs trajectories for the Indian Ocean using OSCAR model.....................265 F.1 Simulation in the Earth Space center..................................................................................266 G. Simulation of FADs trajectories for the Eastern Pacific Ocean using OSCAR model.....270 H. Paper published in Remote Sensing of the Environment....................................................274 List of Figures 1 Tuna fishing grounds location in the Indian Ocean (in number of observations) from September to November (upper panel) and eddy kinetic energy EKE on the fishing grounds (bottom panel)..............................................................…………………………............................................59 2.1 Hovmöller diagram of the sea surface chlorophyll-a for the Pacific Ocean (left) and the Indian Ocean (Right) during the period 2003-2010……...............................................................................62 2.2 Hovmöller diagram of the sea surface temperature for the Pacific Ocean (left) and the Indian Ocean (Right), during the period 2003-2010……............................................................................. 63 2.3 Indian Ocean study area. SLA fields during the South western monsoon season. Currents vectors overlapped Great Whirl (GW), West Indian Coastal Current (WICC), South Monsoon Current (SMC), Southern Gyre (SG). Northeast and Southeast Madagascar Current (NEMC, SEMC), East African Coastal Current (EACC), Agulhas Current (AC), South Equatorial Current (SEC), East Gyral Current (EGC), South Java Current (SJC), Halloway Current (HC), Indonesian Throughflow (ITF) and Leeuwin Current (LC) (currents vectors taken from Schott, 2009 and Fieux, 2001)……...........................................................................................................................................64 2.4 Sea Leval Anomaly (SLA) topography in the EPO with principal currents overploted. California current (CC), North Equatorial Current (NEC), North Equatorial Counter Current (NECC), Costa Rica Coastal Current (CRCC), South Equatorial Current (SEC), Humboldt Current (HC). Costa Rica Dome (CRD)…......................................................................................................................... 65 2.5 Indian Ocean sea surface temperature during the North Eastern Monsoon, December-Mars, (left) and the South Western Monsoon, June-September, (right). Surface currents for each season were overploted (blue arrows)........................................................................................................... 66 2.6 Pacific Ocean sea surface temperature (°C) during March (left) and October (right) 2008........67 2.7 Annual mean thermocline depth (20°C isotherm depth) in the region of Costa Rica Dome. Data from World Ocean Atlas 1998 (Conkright et al., 1999). (Taken from Fiedler, 2002)........................69 2.8 Sea surface temperature (left), sea surface chlorophyll (center) and eddy kinetic energy (right) in the Pacific of Central America in February (average 2003-2012). CRD: Costa Rica Dome, PB: Panama Bight......................................................................................................................................70 2.9 Average chlorophyll concentration (2003-2012) in the Peruvian coastal upwelling in April, during the maximum seasonal intensity of the upwelling.... ….........................................................70 2.10 Mixed layer depth in (a) January and (b) July, based on a temperature difference of 0.2°C from the near-surface temperature. Source From deBoyer Montégut et al. (2004). (c) Averaged maximum mixed layer depth, using the 5 deepest mixed layers in 1° × 1° bins from the Argo profiling float data set (2000-2009) and fitting the mixed layer structure as in Holte and Talley (2009)................72 2.11 Oxygen concentration in the world oceans in micro-moles per liter. Nor the low values in the north-western Indian Ocean and the Eastern Pacific Ocean (Taken from the World Ocean Review, 2010)...................................................................................................................................................73 2.12 Oxygen distribution (µM) at depth where oxygen concentration is minimal, indicating the extend of the OMZ (red color) according to WOA205 climatology. Color bar corresponds to a 1 ± 2 µM interval between 0 and 20 µM, and a 20 ± 2 interval between 20 and 340 µM. The isolines indicate the limit of the upper OMZ CORE depth in meters with a 100m contour interval. (Source Paulmier and Ruiz-Pino, 2009). Note the important levels in the Indian ocean, Arabian sea (AS), Bay of Bengal (BB) and the Pacific Ocean, Eastern SubTropical North Pacific (ESTNP), Eastern Tropical North Pacific, Eastern South Pacific. Eaastern North Pacific (ENP)..................................74 2.13 Oxygen distribution (µM at ± 2 intervals) at depth where oxygen concentration is minimal, according to WOA205 climatology and the CRIO criterion (O2 < 20 µM in the CORE) applied in spring (a), summer (b), fall (c) and winter (d). Note the important levels in the Indian ocean Arabian sea (AS), Bay of Bengal (BB) and in the Pacific Ocean Eastern SubTropical North Pacific (ESTNP), Eastern Tropical North Pacific, Eastern South Pacific. Eastern North Pacific (ENP). (Source Paulmier and Ruiz-Pino, 2009).............................................................................................75 2.14 Time-mean eddy kinetic energy (cm 2 /s 2 ) calculated from drifters observations. Values are shown at 1 degree resolution (R. Lumpking, NOAA/AOML)...........................................................77 2.15 Eddy Kinetic Energy (EKE) in the Indian Ocean during the south-west monsoon. The countries in the north-east of the basin are Oman (O) Yemen (Y), Somalia (S), India (I) and in the south-east of the basin are Mozambique (Mz), South Africa (SA) and Madagascar (M).................78 2.16 Eddy Kinetic Energy (EKE) in Central America during the month of march and the October 2010. The countries in the northern and southern Pacific Ocean's hemispheres are Mexico (M) with the Gulf of Tehuantepec (G.T), Costa Rica (CR) with the Gulf of Papagayo (G.Pp) and Panama (P) with the Gulf of Panama (G.P). Peru (Pr) and Chile (C)....................................................................79 2.17 Indian Ocean Dipolo, positive phase (upper panel), negative phase (bottom panel) (taken from E. Paul Oberlander, Woods Hole Oceanographic Institution)............................................................81 2.18 Schematic effect of "La Niña", normal conditions and "Niño" events in the EPO, on the SST and thermocline structure (Taken from NOAA TAO Diagrams)......................................................81 3.1 Purse seine activity (Source http //www.wandering101photography.com).................................84 3.2 Tuna purse seine vessel (left) (source FAO) and purse seine net (right) (source NOAA).........84 3.3 3D diagram of a Payao anchored FAD (left) (source FAO), bamboo payao on the ocean surface (right) (source ISSF)...........................................................................................................................87 4.25 Number of observations of single fishing sets..........................................................................115 4.26 Kernel density of the same catches, using a spatial radius of 100 km......................................115 4.27 Result of partially spatial random positions, from left to right. Original positions, random with a range of 50 km, 150 km and 250 km respectively.........................................................................115 5.1 Mean wind velocity (June) and surface currents related to the three study areas Morocco, Mozambique Channel and north-western Australia. Atlantic Ocean currents Azores Current (AC), Canary Current (CC), North Equatorial Current (NEC), North Equatorial Counter Current (NECC). Indian Ocean currents Great Whirl (GW), West Indian Coastal Current (WICC), South Monsoon Current (SMC), Southern Gyre (SG). Northeast and Southeast Madagascar Current (NEMC, SEMC), East African Coastal Current (EACC), Agulhas Current (AC), South Equatorial Current (SEC), East Gyral Current (EGC), South Java Current (SJC), Halloway Current (HC), Indonesian Throughflow (ITF) and Leeuwin Current (LC)................................................................................125 5.2 Effects of the preliminary smoothing of 4 km resolution SST data on the front detection. Weak fronts are defined for the gradient interval 0.02-0.042 °C km -1 (gray background) and strong fronts for gradients > 0.042 °C km -1 . The data correspond to the average of five clear images for each of the three study areas, Morocco, Mozambique Channel and north-western Australia (i.e., totaling 15 images). (a-b) The total number of frontal pixels for the three detection window sizes, 32×32 (left column), 24×24 (middle column) and 16×16 pixels (right column) and the three filters, Gaussian (blue dashed line), median (red line) and average (green dashed line) applied at four kernel sizes, 3×3, 5×5, 7×7 and 9×9 pixels. Data for images where no smoothing was performed are labeled "no conv." (black dots). The "reference" or standard smoothing (3×3 median W32), generally used in front detection, is represented by black squares. The red and green squares show the best quantitative and visual results for both weak and strong fronts, obtained with median 7×7 and 5×5 respectively. The images show front detections for north-western Australia on November 24 th , 2009 for (c) 3×3, (d) 5×5, (e) 7×7 and (f) 9×9 pixel kernel sizes for the the median filter at window sizes of W32 (left column), W24 (middle left column) and W16 (middle right column) and the average filter at the W16 window size (right column). ….............................................................................130 5.3 Average frontal length in kilometers for (a) weak and (b) strong fronts, without convolution (black dots) and for each combination of filter type (Gaussian, median and average), kernel (3×3, 5×5, 7×7 and 9×9 pixels) and window size (32×32, 24×24 and 16×16 pixels), as in Figure 2a and b. …......................................................................................................................................................131 5.4 SST and associated fronts detected in November 24 th 2009 in north-western Australia at (a) 1 km resolution with standard "reference" parameters (i.e., a median filter with a 3×3 pixel kernel and a window size of 32×32 pixels); and (b) 4 km resolution with the optimal smoothing combination of a median filter with kernel sizes of 5×5 and 7×7 for strong and weak fronts respectively, and window size of 16×16......................................................................................................................132 5.5 (a-b) Improvement in the detection of thermal fronts between window sizes of 32x32 (left) and 16x16 pixels (right) in the Moroccan upwelling region in February (average 2003-2012) using the optimal smoothing combination of a median filter, kernel sizes of 5×5 for strong fronts and 7×7 for weak fronts. (c-d)The black rectangles highlight the areas where substantial improvements in front detection using the 16x16 window size were observed over the shelf in a complex coastal environment. The 200 m isobath is superimposed in the zoom frames (bottom).............................133 5.6 Histograms of the Sobel gradient associated to frontal pixels for the Moroccan region from daily sea surface temperature (SST) data (2003-2012). (a) Sobel gradients for 1 km, 2 km and 4 km SST data for 16x16 pixel window size, (b) SST gradient values normalized relative to the 4 km data gradient scale and (c) SST gradient variability according to the size of the detection window, i.e., 16×16, 24×24 and 32×32. The populations of frontal pixels associated to weak and strong gradients in (b) and (c) are hatched and striped, respectively, while the left of the histograms indicates pixel gradients that are below the 0.02 °C km -1 threshold.........................................................................134 5.7 (a,b) Sea surface temperature (SST) gradient values used to validate (c-n) front detections computed from smoothed SST data with a 5×5 median filter for strong fronts and a 7×7 median filter for weak fronts for window sizes 32×32, 24×24 and 16×16 for the Moroccan region (c-h) and north-western Australia (i-n). Percentages are expressed as the proportion of fronts detected associated with both weak and strong gradients (in green) at a maximum distance of three pixels. Frontal pixels that are not associated with an SST gradient are shown in red.................................135 5.8 Application of the optimal smoothing combination (median filter, 5×5 kernel for strong fronts, 7×7 kernel for weak fronts, using a detection window of 16x16 pixels) for front detections on 10 years (2003-2012) of daily sea surface temperature (SST) data in the Indian Ocean during the northeast monsoon (December to March) and the south-west monsoon (June to September). (a) Average surface wind field for the same period (Cross-Calibrated Multi-Platform wind product; http //podaac.jpl.nasa.gov/Cross-Calibrated_Multi-Platform_OceanSurfaceWindVectorAnalyses), (b)

F1. 5 F1. 6

 56 figure 7 for corresponding two-dimensional fields).........................................................................131

  Variables temporelles: Année (year), saison climatique (season) (mousson de North-Est, de Sud-Ouest et inter-moussons I et II pour l'OI et Climatique par trimestre Janvier-Mars, Avril-Juin, Juillet-Septembre et Octobre-Décembre pour l'Océan Pacifique Est).

Figure 1 :

 1 Figure 1: Tuna fishing grounds location in the Indian Ocean (in number of observations) from September to November (upper panel) and the eddy kinetic energy EKE (m²/S 2 ) on the fishing grounds(bottom panel).

Figure 2 . 1 :

 21 Figure 2.1: Hovmöller diagram of the sea surface chlorophyll a for the Pacific Ocean (left) and the Indian Ocean (Right) during the period 2003-2010.

Figure 2 . 2 :

 22 Figure 2.2: Hovmöller diagram of the sea surface temperature for the Pacific Ocean (left) and the Indian Ocean (Right), during the period 2003-2010

Figure 2 . 3 :

 23 Figure 2.3: Indian Ocean study area. SLA fields during the South western monsoon season. Currents vectors overlapped: Great Whirl (GW), West Indian Coastal Current (WICC), South Monsoon Current (SMC), Southern Gyre (SG). Northeast and Southeast Madagascar Current (NEMC, SEMC), East African Coastal Current (EACC), Agulhas Current (AC), South Equatorial Current (SEC), East Gyral Current (EGC), South Java Current (SJC), Halloway Current (HC), Indonesian Throughflow (ITF) and Leeuwin Current (LC) (currents vectors taken from[START_REF] Schott | Indian Ocean circulation and climate variability[END_REF][START_REF] Fieux | Current system in the Indian ocean[END_REF].

Figure 2 . 4 :

 24 Figure 2.4: Sea Leval Anomaly (SLA) topography in the EPO with principal currents overploted. California current (CC), North Equatorial Current (NEC), North Equatorial Counter Current (NECC), Costa Rica Coastal Current (CRCC), South Equatorial Current (SEC), Humboldt Current (HC). Costa Rica Dome (CRD).

Figure 2 . 5 :

 25 Figure 2.5: Indian Ocean sea surface temperature during the North Eastern Monsoon, December-Mars, (left) and the South Western Monsoon, June-September, (right). Surface currents for each season were overploted (blue arrows).

Figure 2 . 7 :

 27 Figure 2.7: Annual mean thermocline depth (20°C isotherm depth) in the region of Costa Rica Dome. Data from WorldOcean Atlas 1998 (Conkright et al., 1999). (Taken from[START_REF] Fiedler | The annual cycle and biological effects of the Costa Rica Dome[END_REF]

Figure 2 .

 2 Figure 2.10 Mixed layer depth in (a) January and (b) July, based on a temperature difference of 0.2°C from the nearsurface temperature. Source: From deBoyer Montégut et al. (2004). (c) Averaged maximum mixed layer depth, using the 5 deepest mixed layers in 1° × 1° bins from the Argo profiling float data set (2000-2009) and fitting the mixed layer structure as in Holte and Talley (2009).

Figure 2 . 11 :

 211 Figure 2.11: Oxygen concentration in the world oceans in micro-moles per liter. Nor the low values in the northwestern Indian Ocean and the Eastern Pacific Ocean (Source: the World Ocean Review, 2010).

Figure 2 .

 2 Figure 2.12 Oxygen distribution (µM) at depth where oxygen concentration is minimal, indicating the extend of the OMZ (red color) according to WOA205 climatology. Color bar corresponds to a 1 ± 2 µM interval between 0 and 20 µM, and a 20 ± 2 interval between 20 and 340 µM. The isolines indicate the limit of the upper OMZ CORE depth in meters with a 100m contour interval. Source Paulmier and Ruiz-Pino (2009). Note the important levels in the Indian ocean, Arabian sea (AS), Bay of Bengal (BB) and the Pacific Ocean, Eastern SubTropical North Pacific (ESTNP), Eastern Tropical North Pacific, Eastern South Pacific. Eastern North Pacific (ENP).

Figure 2 .

 2 Figure 2.13 Oxygen distribution (µM at ± 2 intervals) at depth where oxygen concentration is minimal, according to WOA205 climatology and the CRIO criterion (O2 < 20 µM in the CORE) applied in spring (a), summer (b), fall (c) and winter (d). Note the important levels in the Indian ocean: Arabian sea (AS), Bay of Bengal (BB) and in the Pacific Ocean: Eastern SubTropical North Pacific (ESTNP), Eastern Tropical North Pacific, Eastern South Pacific. Eastern North Pacific (ENP). Source Paulmier and Ruiz-Pino (2009).

Figure 2 .

 2 Figure 2.14 Time-mean eddy kinetic energy (cm 2 /s 2 ) calculated from drifters observations. Values are shown at 1 degree resolution (R. Lumpking, NOAA/AOML).

  . The trajectory of these eddies follows a path along the western part of the channel and they eventually merge or are absorbed by the Agulhas Current (de Ruijter et al. 2002) (Fig. 2.15).

Figure 2 .

 2 Figure 2.15: Eddy Kinetic Energy (EKE) in the Indian Ocean during the south-west monsoon. The countries in the north-east of the basin are: Oman (O) Yemen (Y), Somalia (S), India (I) and in the south-east of the basin are: Mozambique (Mz), South Africa (SA) and Madagascar (M).

Figure 2 .

 2 Figure 2.16: Eddy Kinetic Energy (EKE) in Central America during the month of march and the October 2010. The countries in the northern and southern Pacific Ocean's hemispheres are: Mexico (M) with the Gulf of Tehuantepec (G.T), Costa Rica (CR) with the Gulf of Papagayo (G.Pp) and Panama (P) with the Gulf of Panama (G.P). Peru (Pr) and Chile (C).

El

  Niño and La Niña are opposite phases of what is known as the El Niño-Southern Oscillation (ENSO) cycle. The ENSO cycle is a scientific term that describes the fluctuations in temperature between the ocean and atmosphere in the east-central Equatorial Pacific (approximately between the International Date Line and 120 degrees West) (NOAA, 2015) (Fig. 2.18).

Figure 2 . 18 :

 218 Figure 2.18: Schematic effect of "La Niña", normal conditions and "Niño" events in the EPO, on the SST and thermocline structure (Taken from NOAA TAO Diagrams)

Figure 3 . 1

 31 Figure 3.1 Purse seine activity (Source: http://www.wandering101photography.com)

Figure 3 . 5 :

 35 Figure 3.5: Tree main stages of purse seining operations, a. Setting the seine, b. Pursing the seine, c. Hauling the seine. (Source (Pravin 2002))

Figure 3 . 6 :Figure 3 . 7

 3637 Figure 3.6: Tropical tuna principal morphological characteristics. a) Yellowfin tuna, common names: Yellowfin tuna (USA), Albacore (France), Rabil (Spain) or Atun Aleta amarilla (LatinAmerica). b) skipjack tuna, common names: Skipjack tuna (English), Listao (French), Listado, Barrilete (Spanish) and c) bigeye tuna, common names: Bigeye tuna (English), Thon obèse/Patudo (French), Patudo (Spanish).

Figure 4 . 1 :

 41 Figure 4.1: Latitudinal variability of tropical tuna catches in the Indian ocean for 4 temporal strata or quarters: Q1, Q2, Q3, Q4 (left) and climatic seasons: North-East monsoon, Intermediate monsoon I, South-West monsoon and Intermediate monsoon II (center). Spatial areas showing the intensity of the catch during each season.

Figure 4 . 2 :

 42 Figure 4.2: Average catch size in weight taken by the EU purse seine fishery in the Indian Ocean during 1990-2006 by species, yellowfin tuna (YFT) (left), bigeye tuna (BET) (right). (Taken fromFonteneau, 2007)

Figure 4 . 3 :

 43 Figure 4.3: Average weight (kg) for the three species caught on Free school

Figure 4 . 4 :

 44 Figure 4.4: Average weight (kg) for the three species caught under FADs

Figure 4 . 5 :

 45 Figure 4.5: Relative abundance of tropical tuna in the Indian ocean during the period 2003-2010. Free school (left) and FADs (right) from the SPECIES database.

Figure 4 . 6 :

 46 Figure 4.6: Capture (metric tonnes) per year and climatic season of the study period (left) and Number of boats (blue line) and sets (red line) per year of the purse seine fishery in the Indian Ocean (right).

Figure 4 . 7 :Figure 4 . 8 :Figure 4 . 9 :

 474849 Figure 4.7: Location of catches on FS (blue circles) and FADS (red circles) forYFT (2003YFT ( -2010) )

Figure 4 .

 4 Figure 4.10: Tuna presence (left) and absence (right) in the single catch sets during the 2003-2010. YFT (orange), SKJ (Blue), BET (red). Catches for both fishing modes (Fee school and FADs) are mixed.

Figure 4 .

 4 Figure 4.11: El Niño anomalies during the period of study during 2007 and 2008, red color shows El Niño events and blue La Niña events. Values 2008 indicate La Niña moderate event (Source NOAA)

Figure 4 . 12 :

 412 Figure 4.12: Relative abundance of YFT (yellow color), SKJ (blue color) and BET (red color) caught on free school, FADs and LOGs (upper panel) and example of the yellowfin tuna distribution by type of set in the Eastern Pacific Ocean (EPO), during 2007 -2008.

Figure 4 . 13 :

 413 Figure 4.13: Relative sizes proportions (small, medium and large) for YFT in the EPO by set type/fishing mode. Note that size ranges are different from those found for the Indian Ocean.

Figure 4 . 14 :

 414 Figure 4.14: Relative sizes proportions (small, medium and large) for SKJ in the EPO by set type/fishing mode. Note that size ranges are different from those found in the Indian Ocean.

Figure 4 .

 4 Figure 4.15: Relative sizes proportions (small, medium and large) BET in the EPO by set type/fishing mode. Note that size ranges are different from those found in the Indian Ocean.

Figure 4 .

 4 Figure 4.16: Tuna presence (left) and absence (right) in the single catch sets during the 2007-2008 period in the EPO. YFT (orange), SKJ (Blue), BET (red). Catches for three sets types/fishing modes (School, Logs and FADs) are mixed.

Figure 4 .

 4 Figure 4.19: Example of eddy kinetic energy showing the Eastern Pacific Ocean (EPO) and the Indian Ocean (IO) study areas.

Figure 4 . 24 :

 424 Figure 4.24: Computation of distance to fronts: (a) splitting fronts into weak and strong intensity (b) computation of associated front probability (c) resulting distance (km). Example for the IO.

Figure 5 . 1 :

 51 Figure 5.1: Mean wind velocity (June) and surface currents related to the three study areas: Morocco, Mozambique Channel and north-western Australia. Atlantic Ocean currents: Azores Current (AC), Canary Current (CC), North Equatorial Current (NEC), North Equatorial Counter Current (NECC). Indian Ocean currents: Great Whirl (GW), West Indian Coastal Current (WICC), South Monsoon Current (SMC), Southern Gyre (SG). Northeast and Southeast Madagascar Current (NEMC, SEMC), East African Coastal Current (EACC), Agulhas Current (AC), South Equatorial Current (SEC), East Gyral Current (EGC), South Java Current (SJC), Halloway Current (HC), Indonesian Throughflow (ITF) and Leeuwin Current (LC).

Figure 5 . 2 :

 52 Figure 5.2: Effects of the preliminary smoothing of 4 km resolution SST data on the front detection. Weak fronts are defined for the gradient interval 0.02-0.042 °C km -1 (gray background) and strong fronts for gradients > 0.042 °C km -1 . The data correspond to the average of five clear images for each of the three study areas, Morocco, Mozambique Channel and north-western Australia (i.e., totaling 15 images). (a-b) The total number of frontal pixels for the three detection window sizes, 32×32 (left column), 24×24 (middle column) and 16×16 pixels (right column) and the three filters, Gaussian (blue dashed line), median (red line) and average (green dashed line) applied at four kernel sizes, 3×3, 5×5, 7×7 and 9×9 pixels. Data for images where no smoothing was performed are labeled "no conv." (black dots). The "reference" or standard smoothing (3×3 median W32), generally used in front detection, is represented by black squares. The red and green squares show the best quantitative and visual results for both weak and strong fronts, obtained with median 7×7 and 5×5 respectively. The images show front detections for north-western Australia on November 24 th , 2009 for (c) 3×3, (d) 5×5, (e) 7×7 and (f) 9×9 pixel kernel sizes for the the median filter at window sizes of W32 (left column), W24 (middle left column) and W16 (middle right column) and the average filter at the W16 window size (right column).

Figure 5 . 3 :

 53 Figure 5.3: Average frontal length in kilometers for (a) weak and (b) strong fronts, without convolution (black dots) and for each combination of filter type (Gaussian, median and average), kernel (3×3, 5×5, 7×7 and 9×9 pixels) and window size (32×32, 24×24 and 16×16 pixels), as in Figure 2a and b.

Figure 5 . 4 :

 54 Figure 5.4: SST and associated fronts detected in November 24 th 2009 in north-western Australia at (a) 1 km resolution with standard "reference" parameters (i.e., a median filter with a 3×3 pixel kernel and a window size of 32×32 pixels); and (b) 4 km resolution with the optimal smoothing combination of a median filter with kernel sizes of 5×5 and 7×7 for strong and weak fronts respectively, and window size of 16×16.

Figure 5 . 5 :

 55 Figure 5.5: (a-b) Improvement in the detection of thermal fronts between window sizes of 32x32 (left) and 16x16 pixels (right) in the Moroccan upwelling region in February (average 2003-2012) using the optimal smoothing combination of a median filter, kernel sizes of 5×5 for strong fronts and 7×7 for weak fronts. (c-d)The black rectangles highlight the areas where substantial improvements in front detection using the 16x16 window size were observed over the shelf in a complex coastal environment. The 200 m isobath is superimposed in the zoom frames (bottom) .

Figure 5 . 6 :

 56 Figure 5.6: Histograms of the Sobel gradient associated to frontal pixels for the Moroccan region from daily sea surface temperature (SST) data (2003-2012). (a) Sobel gradients for 1 km, 2 km and 4 km SST data for 16x16 pixel window size, (b) SST gradient values normalized relative to the 4 km data gradient scale and (c) SST gradient variability according to the size of the detection window, i.e., 16×16, 24×24 and 32×32. The populations of frontal pixels associated to weak and strong gradients in (b) and (c) are hatched and striped, respectively, while the left of the histograms indicates pixel gradients that are below the 0.02 °C km -1 threshold.

Figure 5 .

 5 Figure 5.7. (a,b) Sea surface temperature (SST) gradient values used to validate (c-n) front detections computed from smoothed SST data with a 5×5 median filter for strong fronts and a 7×7 median filter for weak fronts for window sizes 32×32, 24×24 and 16×16 for the Moroccan region (c-h) and north-western Australia (i-n). Percentages are expressed as the proportion of fronts detected associated with both weak and strong gradients (in green) at a maximum distance of three pixels. Frontal pixels that are not associated with an SST gradient are shown in red.

Figure 5 . 8 :

 58 Figure 5.8: Application of the optimal smoothing combination (median filter, 5×5 kernel for strong fronts, 7×7 kernel for weak fronts, using a detection window of 16x16 pixels) for front detections on 10 years (2003-2012) of daily sea surface temperature (SST) data in the Indian Ocean during the north-east monsoon (December to March) and the southwest monsoon (June to September). (a) Average surface wind field for the same period (Cross-Calibrated Multi-Platformwindproduct; http://podaac.jpl.nasa.gov/Cross-Calibrated_Multi-Platform_OceanSurfaceWindVectorAnalyses), (b) SST with the main currents (current abbreviations as in Figure 1) and occurrence of thermal fronts of (c) weak and (d) "strong" intensity. The Longhurst (2010) ecological provinces are superimposed.

Figure 6 . 1 :

 61 Figure 6.1: modeling scheme constructed to analyze the environment conditions for yellowfin tuna (YFT), skipjack tuna (SKJ) and bigeye tuna (BET) fishing grounds, derive from single set tuna catch records in the Indian Ocean.Catch models (green area), random models (gray area) and dominance models (yellow area) show the model ID (1 to 16), the size of the species (small, large and the unique size for SKJ) the fishing mode related (FS or FADs) and the total percentage of explanation. For BRT general models (1 and 2) the percentage of contribution of the more important predictor, size, is shown.

Table 6 . 4 : 6 . 5 :Figure 6 . 2 :

 646562 Figure 6.2: Boosted Regression Tree fitted function for yellowfin tuna (YFT) general model (1) with 72% explained variance, red line shows the smooth function for continues variables (left). Relative contributions of all predictors (right).

Figure 6 . 3 :

 63 Figure 6.3: Boosted Regression Tree fitted function for Skipjack tuna (SKJ) general model (2) with 74% explained variance, red line shows the smooth function for continues variables (left). Relative contributions of all predictors (right).

Figure 6 . 4 :

 64 Figure 6.4: Boosted Regression Tree fitted function for Bigeye tuna (BET) general model 3 with 74% explained variance, red line shows the smooth function for continues variables (left) . Percentages are relative to the total percentage of the model. Relative contributions of all predictors (right).

Figure 6 . 5 :

 65 Figure 6.5: Relative contribution of the variables (in %) for small-sized tunas caught on free school (left). Net diagram of the contributions (right).

Figure 6 . 6 :

 66 Figure 6.6: BRT fitted function for small-sized yellowfin tuna (sYFT) on free school (FS).

Figure 6 . 7 :

 67 Figure 6.7: BRT fitted function for small-sized bigeye tuna (sBET) on free school (FS).

Figure 6 . 9 :

 69 Figure 6.9: Relative contribution (in %) of the variables for large-sized tunas (left) on free school. Net diagram of the contributions (right).

Figure 6 .Figure 6 .

 66 Figure 6.10: BRT fitted function for large-sized yellowfin tuna on free school (FS).

Figure 6 .

 6 Figure 6.14: BRT fitted function for small-sized yellowfin tuna on associated school (FADs).

Figure 6 .

 6 Figure 6.15: BRT fitted function for small-sized bigeye tuna on associated school (FAD)s.

Figure 6 .

 6 Figure 6.16: Bathymetry, with 3000 m (light black line) and 4000 m (dark black line) contours superimposed. Bigeye tuna catches are overploted (red points).

Figure 6 . 17 :

 617 Figure 6.17: Relative contribution (in %) of the variables for large-sized tunas (left) on FADs. Net diagram of the contributions (right).

Figure 6 .

 6 Figure 6.18: BRT fitted function for largel-sized yellowfin tuna on associated school (FAD)s.

Figure 6 .

 6 Figure 6.19: BRT fitted function for large-sized bigeye tuna on associated school (FAD)s.

Figure 6 .

 6 Figure 6.21: BRT fitted function for small-sized SKJ individuals caught on FADs.

Figure 6 .

 6 Figure 6.22: BRT output (upper panel) and histograms of Chla for FADs (red line) and free school (blue line) (bottom panel). See appendix C.

Figure 6 .

 6 Figure 6.23: Sea surface chlorophyll (left), note 0.2 and 0.4 (mg/m3) contour and thermal fronts (right) with SKJ tuna catches overploted (white stars).

Figure 6 .Figure 6 .

 66 Figure 6.25: Boosted Regression Tree fitted function for Dominance, small sized individuals on FADs model 13: sYFT (upper panel) and model 14: sBET (bottom panel)

Figure 6 . 27 :

 627 Figure 6.27: Conceptual model of the relationships observed between Distance to eddies and Catch by size.

Figure 6 .

 6 Figure 6.28: EKE (left column) and Distance to eddies (right column) for small BET on FS (model 5, upper row), for large BET on FS (model 9, middle row) and for large YFT on FADs (model 10, lower row).

Figure 7 . 2 :

 72 Figure 7.2: Modeling scheme constructed to analyze the environment conditions for yellowfin tuna (YFT) fishing grounds, derived from single set catch records in the Eastern Pacific Ocean. Catch models (green area), random models (gray area) and dominance models (yellow area) with model ID (1 to 11), separated by size (small, medium and large) and then by fishing mode (FS, LOG and FADs). The total percentage of explanation of each model is given.

Figure 7 . 3 :

 73 Figure 7.3: Boosted Regression Tree fitted function for yellowfin tuna (YFT) general model (1) with 41% explained variance, red line shows the smooth function for continues variables (upper panel). Relative contributions of all predictors (bottom panel). Note the similar relative importance of several variables.

  Figure 7.4: Boosted Regression Tree fitted function for small yellowfin tuna (YFT) caught on FS model 2 (upper panel) and for small YFT caught on FADs model 3 (bottom panel) with 37% and 18% of explained variance respectively. Red line shows the smooth function for continues variables. Percentages per variable are relative to the total percentage of the model.

Figure 7 . 9 :

 79 Figure 7.9: Boosted Regression Tree fitted function for medium yellowfin tuna (YFT) caught on LOG model 5 with 33% of explained variance. Red line shows the smooth function for continues variables.

Figure 7 .

 7 Figure 7.10: Barplot of the relative contributions for medium yellowfin tuna (YFT) caught on FADs (model 6) with 20% of explained variance.

Figure 7 . 11 :

 711 Figure 7.11: Boosted Regression Tree fitted function for large yellowfin tuna (YFT) caught on FS model 7 with 39% of explained variance and relative contributions in percentage. Red line shows the smooth function for continues variables.

Figure 7 . 13 :

 713 Figure 7.13: Boosted Regression Tree fitted function for large yellowfin tuna (YFT) caught on FADs model 8 with 35% of explained variance and relative contributions in percentage. Red line shows the smooth function for continues variables. All percentages are relative to the total explanation of the model.

Figure 7 .

 7 Figure 7.17: Boosted Regression Tree fitted function for medium-sized yellowfin tuna (YFT) caught on FS model 11 with 41% of total explained variance. Red line shows the smooth function for continues variables.

Figure 8 .

 8 Figure 8.1: Chapters of the thesis work

Figure 8 . 4 :

 84 Figure 8.4: Deployment and sets positions (courtesy Dr. Martin Hall, IATTC)

Figure 8 . 5 :

 85 Figure 8.5: Scheme of work already done (yellow) and the future work propose (green) to determine the ecological effects of FADs

5 .

 5 Example of buoys used on FADs. a) MSi_buoy, b) oceania-fish-buoys, c)Maritime-Inst, d) fishing marking radio buoy.

  The Longhurst (2010) ecological provinces are superimposed....................................................................................................................................138 6.1 modeling scheme constructed to analyze the environment conditions for yellowfin tuna (YFT), Relative contribution (in percentage) of the variables for skipjack (left) on FADs. Net diagram of the contributions compared with yellowfin and bigeye tuna small sizes (right)..........................158 6.21 BRT fitted function for small-sized SKJ individuals caught on FADs.....................................158 6.22 BRT output (upper panel) and histogram the fishing grounds distribution for Chla, FADs are shown in red and free school in blue (bottom panel) (see appendix C)...........................................159 6.23 Sea surface chlorophyll (left), note 0.2 and 0.4 (mg/m3) contour and thermal fronts (right) with separated by size (small, medium and large) and then by fishing mode (FS, LOG and FADs). The total percentage of explanation is given between parenthesis...................................................171 7.3 Boosted Regression Tree fitted function for yellowfin tuna (YFT) general model (1) with 41% explained variance, red line shows the smooth function for continues variables (upper panel). Relative contributions of all predictors (bottom panel). Note the similar relative importance of several variables...............................................................................................................................176 7.4 Boosted Regression Tree fitted function for small yellowfin tuna (YFT) caught on FS model 2 (upper panel) and for small YFT caught on FADs model 3 (bottom panel) with 37% and 18% of Barplot of relative contributions for medium yellowfin tuna (YFT) caught on FADs model 6 with 20% of explained variance.......................................................................................................180 7.11 Boosted Regression Tree fitted function for large yellowfin tuna (YFT) caught on FS model 7 with 39% of explained variance and relative contributions in percentage. Red line shows the smooth function for continues variables.......................................................................................................181 7.12 Comparison between large-sizes YFT catch levels on FS (discontinuous line) and environmental conditions of the YFT FS fishing grounds, in density, for SST (left) and SSC (right). (See appendix D, fishing grounds histograms for SST and SSC)....................................................181 Histograms (all variables) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS..............................224-246 C2.1 Example of thermal habitat shift from FS (blue circles) to FADs (black circles). SST varies from 25°C to 30° (black outside this range). The inserted plot shows the corresponding density of the SSTs for the same locations........................................................................................................248 C2.2 Example of comparative attractiveness of anticyclonic eddies for FS (blue curves) and FADs Example of forms used by the observers to describe FADs/ buoys deployment and association with natural objects found at the ocean as branch of trees in the Indian Ocean (upper panel). Descriptions of components of the objects found in the ocean as wood in the Pacific ocean (bottom panel)............................................................................................................................................... 262 E4.3 Analyses of the vessel trajectories during the fishing campaign..............................................263 E4.4 Description of some of the buoys used on FADs until.............................................................263 E4.5 Example of buoys used on FADs. a) MSi_buoy, b) oceania-fish-buoys, c)Maritime-Inst, d) fishing marking radio buoy...............................................................................................................264

skipjack tuna (SKJ) and bigeye tuna (BET) fishing grounds, derive from single set tuna catch records in the Indian Ocean. Catch models (green area), random models (gray area) and dominance models (yellow area) show the model ID (1 to 16), the size of the species (small, large and the unique size for SKJ) the fishing mode related (FS or FADs) and the total percentage of explanation in parenthesis. For BRT general models (1 and 2) the percentage of contribution of the more important predictor, size, is shown...................................................................................................142 6.2 Boosted Regression Tree fitted function for yellowfin tuna (YFT) general model (1) with 72% explained variance, red line shows the smooth function for continues variables (left). Relative contributions of all predictors (right)................................................................................................148 6.3 Boosted Regression Tree fitted function for Skipjack tuna (SKJ) general model (2) with 74% explained variance, red line shows the smooth function for continues variables (left). Relative contributions of all predictors (right)................................................................................................148 6.4 Boosted Regression Tree fitted function for Bigeye tuna (BET) general model 3 with 74% explained variance, red line shows the smooth function for continues variables (left). Relative contributions of all predictors (right)................................................................................................149 6.5 Relative contribution of the variables for small-sized tunas catch on free school (left) (in percentage). Net diagram of the contributions (right).............................................................. .......150 6.19 BRT fitted function for large-sized bigeye tuna on associated school (FAD)s........................157 6.20 SKJ tuna catches overploted (white stars).......................................................................................159 6.24 BRT fitted function for small-sized YFT individuals caught on FS.........................................160 6.25 Boosted Regression Tree fitted function for Dominance, small sized individuals on FADs model 13: sYFT (upper panel) and model 14: sBET (bottom panel)...............................................162 6.26 Boosted Regression Tree fitted function for Dominance models on FADs, large YFT and SKJ (model 15, 38% and 16, 46%)..........................................................................................................163 6.27 Conceptual model of the relationships observed between Distance to eddies and Catch by size....................................................................................................................................................165 6.28 EKE (left column) and Distance to eddies (right column) for small BET on FS (model 5, upper row), for large BET on FS (model 9, middle row) and for large YFT on FADs (model 10, lower row).........................................................................................................................................166 6.29 Boosted Regression Tree fitted function for Random model 4.1 (28%)..................................167 7.1 Yellowfin tuna fishing grounds respectively for school (blue), log (green) and fads (red) fishing modes in the EPO.............................................................................................................................170 7.2 Modeling scheme constructed to analyze the environment conditions for yellowfin tuna (YFT) fishing grounds, derived from single set catch records in the Eastern Pacific Ocean. Catch models (green area), random models (gray area) and dominance models (yellow area) with model ID (1 to 11), explained variance respectively. Red line shows the smooth function for continues variables.......177 7.5 SST from model 3 with its distributions on YFT fishing grounds overploted (left). SSC from model with its distribution on the fishing grounds overploted. Note the positive relationship between catch and the conditions imposed by FADs, specially the low level of pigment concentration. (See appendix D, fishing grounds for SST and SSC)..............................................176 7.6 Small-sized YFT caught on FS, blue, (left) and caught on FADs, red, (right) over SSC field for 2007............................................................................................................................................... ..178 7.7 Boosted Regression Tree fitted function for medium yellowfin tuna (YFT) caught on LOG model 4 with 40% of explained variance. Red line shows the smooth function for continues variables............................................................................................................................................179 7.8 YFT medium-sized catches (withe circles) over strong frontal occurrence field for 2007(left) and zoom of the Costa Rica Dome area (right) . Note how catches on this area are mainly located at distance >400 km from the strong coastal fronts. This pattern is different to what is observed in the Peru-upwelling area where catch are located close to the coast.......................................................179 7.9 Boosted Regression Tree fitted function for medium yellowfin tuna (YFT) caught on LOG model 5 with 33% of explained variance. Red line shows the smooth function for continues variables...........................................................................................................................................180 7.10 7.13 Boosted Regression Tree fitted function for large yellowfin tuna (YFT) caught on FADs model 8 with 35% of explained variance and relative contributions in percentage. Red line shows the smooth function for continues variables...........................................................................................182 7.14 Comparison between large-sizes YFT catch levels on FADs (discontinuous line) and environmental conditions of the YFT FADs fishing grounds, in density, for SST (left) and SSC (right). (See appendix D)..................................................................................................................182 7.15 Large-sized YFT caught on FS, blue, (left) and caught on FADs, red, (right) over SSC field for 2007..................................................................................................................................................183 7.16 Boosted Regression Tree fitted function for medium-sized yellowfin tuna (YFT) caught on FS model 4.1 with 31% (upper panel) and caught on LOG model, 5.1 30% of explained variance (bottom panel) . Red line shows the smooth function for continues variables.................................184 7.17 Boosted Regression Tree fitted function for medium-sized yellowfin tuna (YFT) caught on FS model 11 with 41% of total explained variance. Red line shows the smooth function for continues variables............................................................................................................................................185 8.1 Chapters of the thesis work........................................................................................................189 8.2 MLD for the EPO fishing grounds ….........................................................................................190 C1.1 -C.12 (red curves) according the school size for small YFT (left) and large BET (right). Density of "random locations" are represented in dotted lines..........................................................................249 D1.1 -D.1.12 Histograms (all variables) for yellowfin tuna (YFT) fishing grounds in the Eastern Pacific Ocean (EPO) extracted from purse seine single set records. Small (left) medium (center) and large (right) sized individuals. The plain curve corresponds to values obtained at documented catch locations, the dotted curve represents values obtained at random positions. Red lines represent the sets made in association with Fish Aggregating Devices (FADs), blue lines represent the sets made on free schools (FS) and green lines represent sets made on natural objects or logs (LOG)....251-256 E1.1 Average number of active FADs per day in the Indian Ocean by trimester since 2007.......... 258 E1.2 Conceptual model of the buoys-FADs data base in 2011 and example of preliminary description made of all the data-base fields, their significance and potential improvements proposed. …......................................................................................................................................................259 E.1.3 FADs description and Data sources (upper panel) and data processing with IDL (Bottom panel)................................................................................................................................................260 E.4.1 Example of forms used by the observers to describe the environmental conditions of the fishing grounds in situ as temperature in the Indian Ocean (upper panel) and currents in the Pacific Ocean (bottom panel).......................................................................................................................261 E.4.2 F1.1 Example of preliminary calculations and comparison of velocities with OSCAR, a)FADs N. Obs during Int. Monsoon, b) FADs mean velocity Int. Monsoon c) Ocean surface currents from OSCAR during the Inter-monsoon October -November 2009......................................................266 F1.2 FADs trajectories (red) and OSCAR simulated trajectories (blue) during the SW monsoon

-Monsoon: North-east Monsoon SIED: Single Edge Detection Algorithm SKJ: Skipjack tuna Skiff: Auxiliary boat in Purse seine fishery SLA: Sea Level Anomaly SPECIES: Somme des Poids Echantillonnées par CatégorIES SSC: Sea surface chlorophyll SSC_Dynm: Sea surface chlorophyll dynamics SST: Sea surface temperature SW-Monsoon: South-west Monsoon Vss. capacity: Vessel capacity Vss. year: Vessel year of construction W: window Wjs: Wyrtki Jets Ws: Wind speed YFT: Yellowfin tuna

  Relative species dominance for FS and FADs...........................................................................161 7.1 Predictor variables used in the BRT models developed in this study for the Eastern Pacific ..............................................................................................................................173 7.4 Relative contribution of the predictors by model and category for the BRT modeling scheme General variables (blue), mesoscale variables (green) and temporal variables (dark yellow). Mean contributions are presented in orange. Note the higher average contributions of the general variables category for all models (column Total category (blue)....................................................................174

	Acronyms
	Ocean (EPO), along with their value ranges, unit of measurement, and brief description..............172 Bathy:Bathymetry
	BET: Bigeye tuna
	BRT: Boosted Regression Tree 7.2 The Pearson's correlation matrix of the environmental variables) used in model 1 'General CMW: combination of multiple windows model' for YFT Eastern Pacific Ocean (EPO).................................................................................172 Disslv. oxygen: Dissolved oxygen
	Dist. A. Eddies: Distances to Anticyclonic eddies 7.3 BRT parameters and fitting method used for the Eastern Pacific Ocean (IO) models. ID Dist. C. Eddies: Distances to Cyclonic eddies
	represent the model number of identification. The parameters include the tree complexity (tc), the Dist. S. Fronts: Distances to strong fronts
	learning rate (lr), the bag fraction (bg) and the number of trees (nt) predefined when cross validation Dist. W. Fronts: Distances to weak fronts
	EKE: Eddy Kinetic Energy EPO: Eastern Pacific Ocean FAD: Fish Aggregating Device FS: Free School IO: Indian Ocean IOWP: Indian Ocean Warm Pool IATTC: Inter-American Tropical Tuna Commission ITCZ: Inter-tropical convergence zone method was used7.5 Relative contribution of the predictors by model and category for the BRT random and LOG: Natural Object or log set
	dominance models. General variables (blue), mesoscale variables (green) and temporal variables MLD: Mixed Layer Depth
	(dark yellow). Mean contributions are presented in orange. Note the higher average contributions of NE
	the general variables category for all models (column Total category (blue)..................................175

6.2 The Pearson's correlation matrix of the environmental variables) used in model 1.'General model' for YFT Indian Ocean (IO)...................................................................................................143 6.3 BRT parameters and fitting method used for the Indian Ocean (IO) models. ID represent the model number of identification. The parameters include the tree complexity (tc), the learning rate (lr), the bag fraction (bg) and the number of trees (nt) predefined when cross validation method was used...................................................................................................................................................143 6.4 Relative contribution (in percentage) of the different groups of variables or predictor for the Catch models....................................................................................................................................146 6.5 Relative contribution (in percentage) of the different groups of variables for the Random and Dominance models...........................................................................................................................147 6.6

•

  Les prises sur dauphins, très communes dans le Pacifique Est sont très rares ou absentes dans les autres océans (Donahue & Edwards 1996); Hall & Roman 2013). Ce type de prise concerne essentiellement des agrégations entre le thon Albacore (Tunnus albacares) et des dauphins des genres Stenella (S. attenuata and S. longirostris) et Delphinus (D. delphis).

	• Les prises sur bois flottant correspondent à un comportement opportuniste du pêcheur (Hall &
	Roman 2013). Les pêcheurs observent des agrégations de thons et d'autres espèces avec des
	branches et des troncs dans des régions d'importantes arrivées de matière organique du continent par
	les rivières, spécialement de régions côtières comme les forêts tropicales, lesquelles transportent
	beaucoup de matériel végétal durant les crues en début de saison des pluies (Hall & Roman 2013).
	• Les Dispositifs Concentrateurs de Poisson (DCP), sont des bouées ou radeaux dérivants ou ancrés
	qui attirent et agrègent les poissons pélagiques, les rendant pus faciles à trouver et à capturer. Il est
	connu que les DCP n'augmentent pas l'abondance totale en poisson, mais la redistribuent sur des
	surfaces spécifiques et généralement plus réduites. Les DCP sont constitués de différents matériaux,
	la plupart d'éléments flottants (habituellement des bambous), ainsi que des cordes, des morceaux de
	filets et du lest (Hall & Roman 2013).

Table 3 .1

 3 Morphological characteristics of the principal tropical tuna species

	Characteristics	Yellowfin tuna (YFT)	Skipjack (SKJ)	Bigeye tuna (BET)

Table 4 . 1 .

 41 Range of values for the classic and mesoscale variables for each study area.

Table 6 . 2 :

 62 The Pearson's correlation matrix of the environmental variables) used in model 1.'General model' for YFT Indian Ocean (IO).

		Bathymetry	SST	WS	MLD	SSC	Chla Dynm Dissolv. Oxygen	EKE	Dist W fronts	Dist S fronts	Dist A eddies Dist C eddies
	Bathymetry		0,02	0,08	0,16	0,20	0,03	0,06	0,10	0,14	0,13	0,10	0,29
	SST	0,02		0,73	0,61	0,56	0,05	0,28	0,06	0,09	0,33	0,09	0,09
	WS	0,08	0,73		0,66	0,40	0,13	0,00	0,16	0,12	0,41	0,16	0,05
	MLD	0,16	0,61	0,66		0,21	0,11	0,22	0,11	0,12	0,28	0,25	0,09
	SSC	0,20	0,56	0,40	0,21		0,16	0,33	0,00	0,12	0,40	0,11	0,15
	Chla Dynm	0,03	0,05	0,13	0,11	0,16		0,12	0,07	0,08	0,03	0,20	0,10
	Dissolv Oxygen	0,06	0,28	0,00	0,22	0,33	0,12		0,06	0,02	0,03	0,03	0,10
	EKE	0,10	0,06	0,16	0,11	0,00	0,07	0,06		0,03	0,02	0,14	0,16
	Dist. W fronts	0,14	0,09	0,12	0,12	0,12	0,08	0,02	0,03		0,29	0,12	0,06
	Dist. S fronts	0,13	0,33	0,41	0,28	0,40	0,03	0,03	0,02	0,29		0,02	0,01
	Dist. A eddies	0,10	0,09	0,16	0,25	0,11	0,20	0,03	0,14	0,12	0,02		0,31
	Dist. C eddies	0,29	0,09	0,05	0,09	0,15	0,10	0,10	0,16	0,06	0,01	0,31

Table 6 . 3 :

 63 BRT parameters and fitting method used for the Indian Ocean (IO) models. ID represent the model number of identification. The parameters include the tree complexity (tc), the learning rate (lr), the bag fraction (bg) and the number of trees (nt) predefined when cross validation method was used.

	ID	MODEL	n	Family	tc	PARAMETERS lr	bg	nt	FITTING METHOD Training / Test Cross validation
	1		2070	Gaussian	5	0,005	0,5		x
	2		1124	Gaussian	5	0,001	0,5		x
	3		1290	Gaussian	5	0,005	0,5		x
	4		1170	Gaussian	5	0,005	0,5		x
	4,1	1170	Gaussian	5	0,005	0,5		x
	5		500	Gaussian	5	0,005	0,5	3500	x
	6		900	Gaussian	5	0,005	0,5		x	x
	6,1	900	Gaussian	5	0,0001	0,5		x
	7		833	Gaussian	5	0,0005	0,5	3000	x
	8		1170	Gaussian	5	0,005	0,5		x
	8,1	1170	Gaussian	5	0,005	0,5		x
	9		500	Gaussian	5	0,005	0,5	3500	x
	10		900	Gaussian	5	0,005	0,5		x	x
	10,1	900	Gaussian	5	0,0001	0,5		x
	11		833	Gaussian	5	0,005	0,5	3000	x
	12		870	Gaussian	5	0,005	0,5
	13		1000	Bernoulli	5	0,001	0,5		x
	14		1000	Bernoulli	5	0,0005	0,5		x
	15		1000	Bernoulli	5	0,001	0,5		x
	16		1000	Bernoulli	4	0,005	0,5		x

sized tunas on FADs (%)

  who found that tunas are associated with FADs, predominantly above the thermocline.

	MODEL ID	10	11
	Large-57	55
		LYFT(FADs)	LBET(FADs)
	SST	10	16
	SSC	4	5
	SSC
	Dynm	1	2
	MLD	11	9
	Dissolv.
	Oxygen	3	4
	Ws	6	4
	Bathy	8	8
	Dist. W
	Fronts	5	4
	Dist. S
	Fronts	6	6
	Dist. A
	Eddies	11	5
	Dist. C
	Eddies	6	3
	EKE	7	12
	F.mode
	Vss.
	Year	3	8
	Vss.
		3	3
	Year	14	11
	Season	4	1
			156

Capacity Classic Mesoscale Fisheries

Table 6 . 6 :

 66 Relative species dominance for FS and FADs.

  6.26).

	n sp	FS (number of sets)	%	FADS (number of sets)	%
	n total	1272		911
	Case 1 sp.	745	59	46	5
	Case 2 sp.	443	35	35	4
	Case 3 sp.	84	7	829	91
					161

Pour les grands poissons pélagiques, comme les thons tropicaux, quatre variables environnementales sont communément utilisées pour caractériser leur habitat : la température de surface (SST) la concentration en chlorophylle de surface (SSC), la hauteur relative de la surface marine (SSH) et la profondeur de la thermocline (MLD). Depuis les années 60, une littérature abondante fait état de la nature des associations entre thons et fronts thermiques dans tous les océans, par exemple pour le Pacifique Nord et Centre (par exemple[START_REF] Mugo | Habitat characteristics of skipjack tuna (Katsuwonus pelamis) in the western North Pacific: a remote sensing perspective[END_REF] Zainuddin et al. 2004a;[START_REF] Zainuddin | Albacore (Thunnus alalunga) fishing ground in relation to oceanographic conditions in the western North Pacific Ocean using remotely sensed satellite data[END_REF], la côte Californienne[START_REF] Fiedler | Tuna aggregation and feeding near fronts observed in satellite imagery[END_REF][START_REF] Laurs | Albacore tuna catch distributions relative to environmental features observed from satellites[END_REF]) et l'Ocean Atlantique Sud[START_REF] Andrade | The relationship between the skipjack tuna (Katsuwonus pelamis) fishery and seasonal temperature variability in the south-western Atlantic[END_REF][START_REF] Zagaglia | Remote sensing data and longline catches of yellowfin tuna (Thunnus albacares) in the equatorial Atlantic[END_REF]), considérant que les structures de méso-échelle, comme les fronts ou les tourbillons, ont un impact marqué sur la distribution des thons. Par exemple, le thon albacore, Thunnus albacares (Bonnaterre, 1788), montre une adaptation de sa stratégie d'alimentation face aux changements environnementaux en étendant son aire d'alimentation dans des régions situées au-delà de sa préférence thermique habituelle[START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF]). D'autre études montrent l'importance des fronts par leur effet combiné en terme de gradient horizontal de température, de salinité et de chlorophylle, par exemple pour le thon albacore[START_REF] Sagarminaga | Spatio-temporal distribution of albacore (Thunnus alalunga) catches in the northeastern Atlantic: relationship with the thermal environment[END_REF]. Néanmoins, extrêmement peu d'études ont tenté de caractériser et surtout de quantifier les structures de méso-échelle via des paramètres dédiés comme la localisation des fronts et de leur paramètres associés.

Acknowledgments

"We don't meet people by accident. They are meant to cross our path for a reason" (Unknown)

Besides the people, I also want to acknowledge the institutions that founding this PhD, the Colombian Institute of Credit and Technical foreign studies (ICETEX), the COLFUTURO foundation, the Institute of Research for the Development (IRD) and the Tuna observatory (IRD, UMR MARBEC).

 [START_REF] Collette | FAO Species Catalogue. Vol. 2. Scombrids of the world. An annotated and illustrated catalogue of tunas, mackerels, bonitos and related species known to date. Rome: FAO[END_REF]Highly migratory species, tuna superbly efficient metabolic system includes a circulatory system that allows them to retain or dissipate heat as required for peak biological performance and efficiency. They are widely distributed over the Atlantic, Indian and Pacific Oceans (Fig. 3.6, 3.7). [START_REF] Nakamura | Tuna: Distribution and migration[END_REF] described two types of migratory movements; the "passive" and the "active"movements. The first happens within a habitat in response to local abiotic or biotic conditions and the second is the movement of fish between habitats following a change in biological conditions during its development from larvae to adult. This last type of behavior is often motivated by the needs of feeding and reproduction. Such migratory movement has been described for many tunas including Skipjack tuna [START_REF] Gauldie | Skipjack velocity, dwell time and migration[END_REF][START_REF] Humston | Schooling and migration of large pelagic fishes relative to environmental cues[END_REF].

Among the most economically important are the tropical tunas; Yellowfin Tuna (Thunnus albacares), Skipjack tuna (Katsuwonus pelamis) and Bigeye tuna (Thunnus obesus). These species also contribute to meeting worldwide protein requirements, making part of the principal market because of their global importance and their intensive international trade for canning and sashimi. Table 3.1 show the most important morphological characteristics of these species.

Yellowfin Tuna (Thunnus albacares (Bonnaterre, 1788))

An epipelagic and mesopelagic species, yellowfin tuna has been described by [START_REF] Sund | Tunas and their environment in the Pacific Ocean: a review[END_REF] as a cosmopolitan pan-tropical species distributed in warm-temperate regions. Studies conducted in the eastern tropical Pacific and central Pacific by Carey &[START_REF] Carey | Sonic tracking experiments with tunas[END_REF][START_REF] Holland | Physiological and behavioural thermoregulation in bigeye tuna (Thunnus obesus)[END_REF] indicate that the vertical movements of yellowfin tuna are restricted predominantly from the surface mixed layer to the top of the thermocline. Previous studies also indicate that yellowfin tuna can occasionally reach 350 m during deep dives for short duration (Bard, 1999). Preference for the mixed layer depth has been observed in small-sized individuals, with presence in a temperature range between 19 and 26°C [START_REF] Block | Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range[END_REF].

Indian Ocean

Here we first described the method applied from the early eighties by (Fonteneau 2007) in order to correct the species composition and size of the tropical tuna. The byproduct derived from this method was the creation of the SPECIES data base of the "Observatoire thonier", an IRD program, used in this study.

Multi-species sampling method: SPECIES Data base

The multi-species sampling method is a procedure applied to the purse seine data since 1980 in the Atlantic ocean, since 1983 in the Indian Ocean and in the Eastern Pacific Ocean since 2000 when the rate of observers, in this last ocean, in the vessels reach 100%. The species composition of all the purse seine catches in the Indian ocean and in the Eastern Pacific have been fully corrected. This is made using the results of these large scale multi-species port samplings that have been simultaneously targeted the size distribution and the species composition of the landed tuna catches (Fonteneau 2007). The main objective of the method is the correction of the bias observed in the reports delivered by captains on catches by species affected, above all, specimens of small sizes mainly yellowfin and bigeye tuna [START_REF] Pianet | New Sampling and Data Processing Strategy for Estimating the Composition of Catches by Species and Sizes in the European Purse Seine Tropical Tuna Fisheries[END_REF].

From an homogeneous sets sample of ~500 fishes landed from the well, tuna are measured except Skipjack (only 50 fishes are measured) because its consistent size distribution. These procedure is conducted by well trained technicians in each ocean, corking under a close permanent control of a scientist. The quality controls are made from time to time in order to check the quality of the sampling in terms of identifications and size measures (Fonteneau 2007).

The data processing require the identification of a strata within which the size and species composition of the catches are consider to be homogeneous. This will be made for a given fishing mode (FADs or Free school) and for a size category (small or large tunas). Two methods are proposed for this identification: 1) Visual Where u and v are the zonal and meridional geostrophic components, respectively themselves computed from the SLA . Eddy kinetic energy (EKE) indicated the intensity of movement of the water mass and can be considered as a proxy of the boundary between two eddies (Tew Kai & Marsac 2010).

Bathymetry (Bathy)

The Seafloor Bathymetry is obtained from the 1 Arc-Minute Global Relief Model Etopo 1 from the National Oceanic and Atmospheric and Administration (NOAA (http://www.ngdc.noaa.gov/mgg/global/)

The underlying hypothesis, supported by several previous studies, is that a catch event is likely associated with the first type of event, in a quantitative way (i.e. that the strength of this response is associated with a higher catches). Therefore, the parameter kept to quantify this dynamics is the difference of the chlorophyll a concentration during the period from 3 days and 12 days before the catch.

Mixed Layer Depth (MLD)

The Mixed Layer Depth (MLD) data is a global scale annual climatology estimated from a fixed temperature threshold on temperature profiles. This data were acquired from data access of the Institut français de recherche pour l'exploitation de la mer IFREMER. http://www.ifremer.fr/cerweb/deboyer/mld/Surface_Mixed_Layer_Depth.php

Dissolved oxygen (Disslv. oxygen)

Dissolved oxygen (Dissolved O2) monthly climatology at 1 degree of resolution is provided by the National Oceanic and atmospheric Administation center (NOAA) through the World ocean Atlas (WOA) https://www.nodc.noaa.gov/cgi-bin/OC5/WOA09/woa09.pl

Distances to eddies (Dist. Eddies)

For this study data about eddy trajectories and eddy parameter (as location, radius and lifetime) was provided by Dr. Alexis Chaigneau, IRD researcher, who applied his method "Winding Angle" [START_REF] Chaigneau | Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns[END_REF] to Eddy detection. Their results are used to describe most eddies intrinsic characteristics.

The calculation of the distances from the fishing sets to the closest anticyclonic and cyclonic eddy were made through the computation of the border of each eddy using its central position and radius. After that we elaborate images of the eddies distribution and using the fishing sets position we compute the distance (km) to the nearest eddy. It is important to note that sporadic eddies presence between 5°N and 5°S was not considered in this analysis (Figure 4.23).

Chapter 6 6 Tropical tunas and their environment, understanding the role of the mesoscale in the Indian Ocean

This chapter sets out the process we undertook to examine the relationships between the presence of three tropical tuna species, yellowfin tuna (Thunnus albacares; YFT), skipjack tuna (Katsuwonus pelamis; SKJ) and bigeye tuna (Thunnus obesus; BET), considered in this study and their respective environments. It is important to note that in this analysis we only used positive-single set records (precise lat/long locations) to ensure that our results make a description of the true environmental preferences and that they do not describe mixed of environments. This last can happen when several sets are averaged at lesser spatial resolutions (e.g. 1 or 5 degrees) like most of the studies do.

We present here, the multivariate analysis used to identify which environmental conditions determined the distribution of tunas. This consisted in a modeling scheme built in three sections, each characterized by a difference response variable all evaluated by the 12 environmental variables or predictors already mentioned. The response variables are: 1) the exact set-catch locations (log transformed catch data), 2) the random locations and 3) the dominance of tunas caught on FADs (Fig. 6.1). The analysis is focused on four predictor groups which are:

• General or classic variables: Bathymetry (Bathy), sea surface temperature (SST), sea surface chlorophyll (SSC), sea surface chlorophyll dynamics (SSC Dynm), wind speed (WS), mixed layer depth (MLD), and dissolved oxygen (Disslv. Oxygen)

• Mesoscale variables: -Eddy kinetic energy (EKE), distance to weak fronts (Dist. W. fronts), distance to strong fronts (Dist S. fronts), distance to anticyclonic eddies (Dist A. eddies), and distance to cyclonic eddies Dist. C. eddies)

• Fisheries variables: -Fishing mode, vessel capacity (Vess. cap), and vessel year of service (Vess. year); note that these two last variables only applies to IO.

• Temporal variables: Year of the catches (year) and climatic season (season).

. Table 6.1: The predictor variables used in the BRT models developed in this study for the Indian Ocean (IO), along with their their value ranges, unit of measurement, and brief description

Random models by fishing mode and size

In order to keep the number of models as reasonable as possible, we decided to construct four "random models" (for which the catch location in randomly redistributed within the mesoscale space), only for the YFT, whose number of catch record is also superior to the other species. The model for small individuals caught on FS (model 4.1) explains only 28% of the overall variance while models 6.1 (sYFT on FADs), 8.1 (lYFT on FS and 10.1 lYFT on FADs), for small individuals on FADs and for large individuals in both fishing modes explain a negligible part of the catch variability, between 3% and 7%. Consequently, only the first model (4.1) is detailed bellow.

Contrary to the equivalent catch model (model 4), the model 4.1 shows that the most important predictor was the Bathy (14.8%) with a slight positive effect on catches near continental margins (< 2000 m) and a negative effect afterwards. Similarly to catch models, Year was one of the top predictors contributing to 14.5% of the variance, with positive effects in 2005, 2009 and 2010), while SST > 23°C shows a positive relationship up to 29°C, the superior limit of the range observed for the major part of the catch models and reported by [START_REF] Rajapaksha | Multispectral Satellite Data on Ocean Surface to Predict its Vertical Temperature Profiles, a Simple Model[END_REF]. Catches are associated with a relatively narrow range of MLD (40 to 50 m). A moderate positive effect was observed for Dist A. Eddies (i. e., >200 km) and for low EKE values (Fig. 6.24). The overall contribution of the mesoscale parameters is considerably lower than for their equivalent model based on the real position of the catches. This chapter presents the multivariate analyses for yellowfin tuna (Thunnus albacares; YFT) in the Eastern Pacific Ocean (EPO). As described in chapter 4, three categories by weight were considered: small (< 2.5 kg), medium (> 2.5 < 15 kg) and large (>15 kg) as well as three set types ( or fishing modes): Free school (FS), logs (LOG) and fish aggregating devices (FADs). Figure 7.1 shows the fishing grounds for this species by fishing mode. Note how each fishing mode defines specific sub-regions in the EPO and particularly how FADs deeply extend the fishing grounds westward in the equatorial Pacific.

Environmental description of the fishing grounds

As for the IO, the same description of the environmental conditions from the fishing grounds is made for the the Easter Pacific Ocean (EPO), through the analysis of the histograms of twelve variables selected (see appendix C.).

The following tables describe:

• 1) the predictor variables used in the BRT models. Note how the range of all variables is wider than for the Indian Ocean (Table 7.1).

• the Pearson's correlation for the general model, tested for each model. We consider a maximum level of Pearson's correlation of 0.8 to retain a variable in the analyses (Table 7.2).

• 3) the internal BRT parameters and the fitting method for each model (Table 7.3).

Table 7.1: Predictor variables used in the BRT models developed in this study for the Eastern Pacific Ocean (EPO), along with their value ranges, unit of measurement, and brief description.

Table 7.2: The Pearson's correlation matrix of the environmental variables) used in model 1.'General model' for YFT Eastern Pacific Ocean (EPO).

In summary

• We describe the global importance of the mesoscale features in determining tuna habitat in the Indian and in the Eastern Pacific oceans. In the case of fronts, thanks to the definition of specific variables for the first time (i.e., weak and strong fronts).

• We clearly described the different environmental conditions of the FS, LOG and FADs fishing modes.

• We showed the relevance of the fish-size compared with the species composition in terms of the environmental preferences

• We state the necessity to undertake regional studies in areas with different environmental systems as the EPO.

• We propose the use of our method to determine the "FADs history" using the work already done in order to understand the ecological "effects" of FADs.

Technical considerations

Regarding the calculation of distance to eddies we could use the distance to the center of eddies but due to the strong dynamics of FADs observed prior to the analyses where they were found drifting in the eddies contours we decide to calculate only distances to the borders.

Additional considerations

During the course of this thesis work several skills were acquired and the knowledge in different areas (environment, fisheries, technical tools) was improved, I will summarized some of the most important activities that contributed with this work.

Technical skills: (see appendices):

• Environmental data-base management and image processing using IDL

• Fisheries data-base management and data extraction using SQL and ACCESS

• Balbaya and SPECIES data-base (France)

• FADs data-base (France) *the preliminary description of this data base was also done • Observers analyses of the forms and reports • IATTC data-base (USA)

• Statistical skills developed using R

Contacts and contributions

• IATTC-MARBEC to data access • Southwest Fisheries Science Center for BRT modeling aspects.

• Earth and Space center for OSCAR currents model and trajectories simulation.

Yellowfin tuna

We observed that both small and large YFT caught on FADs were associated with a wider SST range (~24°C-31°C) than individuals caught on FS (small YFT:~26°C-30°C and large YFT:~28°C-30°C). This suggests that individuals caught on FADs are significantly found in a wider range of SST and particularly in colder waters than FS. For example, less than 5% of fishing locations of small YFT are made on FS in waters less than 26°C while this proportion reaches 18% under FADs. Very similar differences are found for SKJ. And small BET. "Random locations" show similar patterns for this species, meaning that there is no difference at mesoscale for SST, which is not very surprising for such a parameter (Appendix C1).

Skipjack tuna

Overall, SST preferences for this species were very similar to those exhibited by YFT. The majority of individuals caught on FS were associated with an SST range of 26°C-31°C (Appendix C1)..

Bigeye tuna

For this species, distinct differences in SST preferences were observed between the two size categories. Similarly to YFT and SKJ, small sized individuals caught on FADs have a wider SST preference range, as compared with individuals caught on FS. Interestingly, the distribution pattern for FS in random positions is markedly different, suggesting the interest or a capacity of the small individuals to be in lower temperature waters, maybe for feeding purposes. In contrast, large individuals caught at random positions (both fishing modes) were observed in an almost identical SST range (mode at ~28°C-30°C) (Appendix C1)..

Summary

The differences in SST values observed between the two fishing modes suggest that FAD-associated individuals are drifted into areas that they might not ordinarily swim into, thus exposing them to a wider SST range. This behavior can also be observed in the presence/absence figures presented in Chapter 4, where the spatial distribution of FAD sets do not extend into equatorial regions as much as FS sets. Thus, we hypothesize that FS individuals have a greater ability to remain within the warmer equatorial waters than FAD-associated individuals. Consequently, it is likely that the former group better reflect true temperature preferences in these three species.

It is also interesting to note that large FS individuals of all three species have a preference for warmer waters while small BET (both fishing modes) tolerated lower temperatures. When we compare the true catch positions with the random positions, the distribution patterns are very similar.

Bathymetry

Appendix C1.2: Bathymetry (Bathy) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS.

Yellowfin tuna

For this species, important differences can be observed between the bathymetric preferences of FSand FADs-associated individuals. Small sized individuals caught on FS were observed in a wider range of depths, although a stronger preference is shown for depths of > 3000 m. Random positions are meaningless for bathymetry. We just know that small YFT are likely to be located close to continental slope waters, as also observed for bluefin tuna (Thunnus thynnus; [START_REF] Teo | Oceanographic preferences of Atlantic bluefin tuna, Thunnus thynnus, on their Gulf of Mexico breeding grounds[END_REF]. Conversely, FADassociated individuals were observed to display a stronger preference for deeper depths (i.e., > 3000 m), suggesting that these fish are more likely to be found in offshore regions due to the drifting effect of FADs.

However, large individuals caught on FS also show a shift toward greater depths, possibly linked to changing physiological capabilities. On the other hand, large Individuals caught on FADs are very similar to small sizes, located offshore with higher bathymetry (Appendix C2).

Skipjack tuna

Here, FS individuals display a broader bathymetric preference range than individuals caught on FADs. This latter group show a narrower preference range more towards higher values. There were almost no observed differences between the distribution patterns of the true catch positions and the random positions (Appendix C2).

Bigeye tuna

For this species, strong differences were observed between the two size categories. Small sized individuals caught on FS were found at lower depths than similarly sized individuals caught in association with FADs. However, large sized FS individuals were mainly caught at deeper depths, a pattern that was also noted for large sized FADs-associated individuals (Appendix C2).

Summary

For this variable, there is a strong contrast in the patterns observed for FADs-associated and FS individuals for all three species. Small sized individuals caught on FS (including SKJ) are less offshore. In comparison, large sized FS displayed narrower depth ranges that are more similar to the preference patterns observed for FADs. Overall, individuals caught in association with FADs always related to deeper depths, irrespective of species or size.

Yellowfin tuna

Small and large sized individuals caught on FADs similarly broad ranges of wind speed values. Conversely, FS individuals were observed in a narrower range, with a tendency towards higher speeds for small individuals and lower velocities for large individuals. No significant differences were observed between true catch position and random position distribution patterns for all species, which is not surprising for this parameter that do not display differences at small scale (Appendix C3).

Skipjack tuna

For SKJ, FAD-associated catches showed a slight shift towards higher wind speeds than for FS catches, as observed for YFT (Appendix C3).

Bigeye tuna

For BET higher differences were observed between wind speed for small and large individuals. While ranges for large sized individuals was almost identical between the two different fishing modes, small sized BET caught on FADs had a broader and flatter distribution pattern than similarly sized BET caught on FS (Appendix C3).

Summary

The most important differences were observed between small individual of all species: The flatter pattern observed in association to all FADs indicates that these individuals are associated with a broader range of wind speeds, still showing a "dispersion" effect of FADs in term of habitat, as for many other parameters.

Mixed Layer Depth

Yellowfin tuna

Here, FS individuals in both size categories show an association with a broad range of mixed layer depth values. When compared to the FAD pattern (maximum depth of ∼75 m), it is interesting to note the subtle shift for FS individuals toward the surface. For large individuals, those caught on FADs were associated with a much wider depth range than those caught on FS. Minor difference between true catch position and random position distribution patterns were also noted (Appendix C4).

Skipjack tuna

FS individuals show an association with a narrower range of mixed layer depth values (~60 m) than FADs that, as observed for YFT reach values around 75 m. Patterns of random positions are inverse to those observe for each fishing mode, that means that they show a wider range for FS and a narrow range for FS. This suggest that FADs-associated individuals tend to be in areas with a deeper mixed layer depth (Appendix C4).

Bigeye tuna

For BET, note the slight shift toward lower depth values that can be observed for FS individuals. Note the wide range of values for small sized tunas on both fishing modes being deeper for FADs-associated individuals. There were almost no important differences observed between the distribution patterns of the true catch positions and the random positions .Large individuals caught on FS show a slightly narrow range of values than FADs-associated, here both fishing modes reach values ~ 75 m. Differences with random positions distribution patters are more important for FS individuals due that true catch positions tend to minor depths (Appendix C4).

Summary

For both size categories in all three species, FADs-associated tended to be linked with slightly deeper mixed layer values than FS individuals. Differences with random positions patterns are more relevant for large sized individuals. Overall the maximum depth observed, mainly reach by FADs-associated tunas was 75 m.

Sea surface chlorophyll concentration

Yellowfin tuna

For this variable, FADs-associated, small as well as large, individuals display an association with slightly higher SSC concentrations (> 0.5 mg.m-3). Meanwhile, small FS individuals were associated with the highest concentrations observed (~ 8 mg.m-3), although the overall range of values for this group was much narrower. The maximum SSC concentration value reach for large FS individuals were half of the value observed for small sized tunas (~ 4 mg.m-3). There were almost no observed differences between the distribution patterns of the true catch positions and the random positions (Appendix C5).

Skipjack tuna

For SKJ, a slightly broader range of SSC values were observed for FADs-associated individuals compare with FS catches where maximum values are reached. No differences were observed with random distributed patterns (Appendix C5).

Bigeye tuna

For this species, no important differences were observed between fishing modes for small sized tunas, the main range of distribution are characterized by concentrations < 0.25 mg.m-3. Large FS individuals display a narrower range and a maximum concentration (<0.5 mg.m-3) inferior to the maximum value observed for FADs-associated tunas (>0.5 mg.m-3) (Appendix C5) .

Summary

For both fishing modes, similar maximum SSC concentrations were reached for all species and both sizes. The range of values observed for the two fishing modes was also fairly similar, although it was slightly wider for FAD-associated individuals.

Sea surface chlorophyll dynamics

This variable describes the variation of the SSC concentrations measured in a set area over a defined time period. This parameter is presented has the difference in concentration between Day

Yellowfin tuna

For this species, the random locations distribution patterns are much narrower (closer to zero) than the distribution patterns for the true catch locations, for both size categories and fishing modes. This result indicates that there is less variation in SSC concentrations across the areas different to the fishing grounds. Thus, the effects of the mesoscale structures are both visible and moreover, affecting the two fishing modes in a similar manner. The distribution pattern for small sized individuals caught on FS shows a subtle trend towards positive values (∼0.25), a feature that is not observed in the FADs curve. Minor differences can be observed between the two fishing modes for large sized individuals (Appendix C6).

Skipjack tuna

For SKJ, the distribution pattern of FS individuals suggests they have a subtle preference towards areas with positive values as compared to the distribution pattern of FAD-associated individuals, that shows the opposite effect (Appendix C6).

Bigeye tuna

For this species, clear differences were observed between the distribution pattern for small sized FSand FAD-associated BET. For FS, the range of values are strongly positive, indicating that these individuals have a preference for nutrient-rich waters whilst the negative values observed for FADs represents the increased likelihood that these individuals will drift into more oligotrophic waters. A similar positive trend, albeit much weaker, can be observed for large sized FS individuals (Appendix C6).

Summary

In general, wider ranges of values were observed for the true catch locations as opposed to the random locations (closer to zero) for both fishing modes. For small BET caught on FS, this comparison highlights an important difference, a difference which clearly suggests that a relationship exists between catch and SSC increases. In other words, it means that small BET tend to avoid areas with negative chlorophyll dynamics. At the same time, the negative affects of FADs on this species are evident been associated with negative values, meaning less SSC concentration.

Dissolved Oxygen

Appendix C1.7: Dissolved oxygen (Dissolv. oxygen) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS

Yellowfin tuna

Small and large sized individuals caught on FS are related to lower dissolved oxygen concentrations than FADs-associated fishes that easy reach concentration ~ 5.0 ml/L (Appendix C7).

Skipjack tuna

As with YFT, SKJ caught on FADs were associated with slightly higher oxygen concentrations than individuals caught on FS (mostly <4.7 ml/L). In general, the maximum tolerance levels for this species are noted to be 1.87 ml/L (FL: 50 cm) and 2.16 ml/L (FL:75 cm). Like YFT, the established forage limits for SKJ are defined by dissolved oxygen concentrations above 3.5 ml/L (Brill et al. 2005).

Bigeye tuna

Exactly the same observation as for YFT, ie a general flattening and a shift towards higher values for FADs compared to FS (Appendix C7).

Summary

For all three species and in a similar way for small and large individuals, FADs-associated schools were related to higher oxygen concentrations than those caught on FS. This is may be due to the fact that FADs may be drifted into areas of lower biological activity where less oxygen is consumed by the organisms.

Eddy Kinetic Energy

This parameter is commonly used as a measure of mesoscale variability and thus, can be used to identify regions where mesoscale eddies and/or current meanders commonly appear. Appendix C1.8: Eddy kinetic energy (EKE) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS

Yellowfin tuna

For YFT, there are no visible differences between the distribution patterns of either fishing mode for small sized individuals. For large sized individuals, the FS curve shows a slightly narrower distribution than the FAD curve, with values close to zero ( Appendix C8) .

Skipjack tuna

Almost no differences can be observed in the distribution patterns of EKE values between the two fishing modes( Appendix C8).

Bigeye tuna

For BET, small sized individuals caught on FS show a slightly higher distribution pattern than FADs-associated, with values tapering off around 0.15 m 2 /s 2 . For both fishing modes, large sizes were related with lower EKE values (Appendix C8).

Summary

Overall, no differences in EKE values were observed between the three species, two size categories or either fishing mode (Fig. 6.8).

Distance to weak intensity fronts

Appendix C1.9: Distance to weak intensity fronts (Dist. W. Fronts) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS

Yellowfin tuna

For YFT, short distances to weak fronts (< 100 km) were observed for both fishing modes, with FADs-associated individuals found to occur slightly closer. Overall, this close proximity is probably due to the fact that weak frontal systems are relatively abundant in the study regions. No significant differences were noted between the two size categories (Appendix C9).

Skipjack tuna

For SKJ, individuals caught on FS show a tendency to be located slightly further from weak fronts than individuals caught in association with FADs (Appendix C9).

Bigeye tuna

For BET, we did observe differences between small and large sized individuals caught on FS. We also noted that a wider range of distance values were observed for small sized individuals caught by both fishing modes (Appendix C9).

Summary

Overall, we observed that similar differences existed between the two size categories across the three different species. Small sized individuals caught on FADs (including SKJ) were observed to be on average closer to weak fronts than small sized individuals caught on FS.

This pattern could be explained by the FADs dynamics, which can lead to a higher rate of interactions with this mesoscale structures. This could be specially the case for fronts associated to eddies, for which a lagrangian approach using the geostrophic currents of the mesoscale circulation associated to eddies (FSLE, Finite size Lyapunov Exponents) shows that fronts are commonly associated to convergence lines, where FADs tend to be aggregated. No major differences were observed between the distribution patterns for true catch locations and random positions and this is hard to explain (the same is observed for strong fronts), because it means that distances to fronts do not change at the mesoscale level, up to distances of 250 km (the maximum range of the "random positions").

Distance to strong intensity fronts

Appendix C1.10: Distance to strong intensity fronts (Dist. S. Fronts) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS.

Yellowfin tuna

Small sized YFT caught on FS tended be found closer to strong intensity fronts. Only for this species, the "random positions" show a wider range of distances for small individuals. For large individuals, the range of distance values observed for both fishing modes are very similar (Appendix C10).

Skipjack tuna

For SKJ, a slightly wider range of distance values were observed for individuals caught on FS (Appendix C10).

Bigeye tuna

In contrast to our results for small YFT, small BET caught on FADs were found closer (i.e., smaller distance values) to strong intensity fronts. Again, this observed pattern may be due to the fact that FADs often drift towards the border of coastal currents, where there is a high probability to find a strong intensity fronts (Appendix C10).

Summary

For all three species, the range of 'distance to strong intensity fronts' values ranged between 0 km and 300 km. . This is likely due to the fact that strong fronts occur at lower densities across the study regions and moreover, that they are more likely to be found along the coastal areas (see chapter 5). Differences between fishing modes are larger than for weak intensity fronts, only for BET and specially for small individuals as for the weak fronts, few differences were observed (for both FS and FADs and both size categories) between true and "random" catch locations

Yellowfin tuna

Noticeable differences were observed between the two fishing modes for this species. A wider range of distance values were observed for both small and large sized individuals caught on FADs while narrower curves on FS, mainly for small sized individuals, suggesting that these features are highly attractive. It is surprising to note that the "random locations" appear very significantly closer to the borders of anticyclonic eddies than true sets locations. This point is discussed bellow (Appendix C11).

Skipjack tuna

Similar observations are made between the FS and FADs-associated individuals in true sets locations. Differences with random positions patterns are more relevant .

Bigeye tuna

For BET small sized individuals differences are less evident between FS and FADs-associated. In the case of large sized individuals, higher catch density is observed for both fishing modes, having FADs still a wider preferences for a wider range of distances.. Differences with random positions are clearly observed (Appendix C11).

Summary

For this variable, the key point consists in the differences between the distribution patterns for true catch locations and the distribution patterns for the random positions. On average, the random positions are found to be closer to the outer boundaries of the eddy features than the true positions. This is a surprising result because it suggests that tuna schools would tend to actively "avoid" the proximity of the anticyclonic eddies. This tends to show that the result of the effective attractiveness of these eddies for tunas do not tend to make them close as possible to eddies borders than we could imagine but at a much longer range of distances between 0 and as far as 500 km, where random positions are almost never simulated. This is specially pronounced for the smallest size fraction of all three species. This can also be the consequence of more complex biological interactions in tight relation with the own dynamic of the eddies. Similar observations are made in the multivariate analysis further developed.

Distance to the border of cyclonic eddies Appendix C1.12: Distance to cyclonic eddies (Dist. C. Eddies) for the tropical tuna fishing grounds in the Indian Ocean extracted from purse seine single set records. Small sizes YFT, BET and SKJ unique size (left), large sizes YFT and BET (right). Plain curve corresponds to values obtained at documented catch locations, dotted curve represents values obtained for random positions. Red lines represent the sets made in association with FADs, blue lines represent the sets made on FS

Yellowfin tuna

The results for this species are similar to those found for the variable 'distance to the border of anticyclonic eddies'. Small but noticeable differences were observed between the two fishing modes, with both size categories caught on FS found to be slightly closer to the border of cyclonic eddies than similarly sized individuals caught on FADs (Appendix C12).

Skipjack tuna

For SKJ no strong differences were observed between the two fishing modes. The distribution patter of this species is very similar those observed for the anticyclonic eddies, for true catch positions (Appendix C12).

Bigeye tuna

Here, no differences were observed small and large sized individuals caught either on FS of on FADs. Different patterns are observed between random positions and true sets locations (Appendix C12).

Summary

Our general observations for this variable are exactly the same to those made for the variable 'distance to the border of anticyclonic eddies' (see summary above).

D. Environmental description of the fishing grounds in the Eastern Pacific Ocean Dissolved oxygen

Again, narrower distributions, at higher oxygen concentrations, were observed for all sizes caught on FADs. This pattern was also observed for small and medium LOG-associated YFT while for large sized individuals distributions was as wide as for FS that show a very similar pattern for all sizes with a broader range between 2.5 ml/L and 5.5 ml/L.

Eddy kinetic Energy

For this variable, insignificant differences were observed between all sizes and fishing mode. Meanwhile for large-sized individuals slightly lower values were shown for FAD-associated YFT. This can indicate a higher proximity of this fishing mode to mesoscale structures as eddies.

Distance to weak intensity fronts

FAD fishing mode, for all size categories, was observed to occur at shorter distances to weak fronts (< 200 km) than FS and LOG. A narrow range of distances distribution was also observed for LOG, that also reach longer distances (>500 km). The opposite pattern was observed for FS with an homogeneous distribution up to distances of 600 km. No major differences were observed between true catch locations and random positions.

Distance to strong intensity fronts intensity fronts

YFT caught on FS tend to have a wider distances distribution for all sizes categories (because strong fronts are less evenly distributed in the ocean) but at the same time, tunas tend to be found slightly closer to strong intensity fronts for FS than FAD and LOG-associated fishes. This proximity was better appreciated for large sized individuals. It is also important to note for this size the shorter distances to strong fronts observed for fishes caught on LOG. Overall FAD-associated individuals showed the longest distances to these mesoscale structures. No significant differences were found between random and true catch positions.

Distance to the border of anticyclonic eddies

Strong differences were observed among size categories. Wider range of distance to anticyclonic eddies was observed for small-sized individuals for all fishing modes. Distances were slightly longer for FAD and slightly shorter for LOG-associated YFT. For medium and large-sized fishes narrower distances distribution were found with values < 200 km. The similarities observed between random distribution patterns and the true catch positions is a consequence of the density of eddies in the area that makes possible to be always statistically close to these structures (Figure 4.23, Chapter 4).

Distance to the border of cyclonic eddies

Very Similar pattern were observed for YFT sizes for distance to cyclonic eddies, with the same observations than above.

E. Preliminary work on FADs Trajectories E1. FADs trajectories data

We present here a general analyses of the dynamics of drifting FADs using ocean currents information from the Ocean Surface Current Analyses-Real Time (OSCAR). This is a model that develops a processing system provide operational ocean surface velocity fields from satellite altimeter and vector wind data. Our analyses has two objectives 1) compare real FADs trajectories with general current patterns in the IO and 2) simulate drifting FADs trajectories in the EPO, a region where real trajectory data is not available yet . It is important to note that this work has been made and is available also for the Atlantic Ocean but we present here mainly the results for the Indian and the Pacific oceans.

E2. Method

An extensive research and analyses were carried on in order to have all the elements to understand the dynamics of the drifting FADS. Different methods and approaches was developed taking advantage of the work made by the "Observatoire thonier" at Sète for the Indian and Atlantic ocean FADs. In the other hand a research was made about the observer forms used in the Pacific ocean and a comparison was made and improvement proposed to the observatoire thonier teem. The different approach are explain as follows.