Gaëtan Gilles

Sylvain Richard

 Tixier

D Welsford

M-A Lea

M A Hindell

C Guinet

A Janc

G Richard

N Gasco

G Duhamel

R Arangio

M C Villanueva

L Suberg

P Burch

K Olson

J P Y Arnould Commercial

Keywords: depredation, demersal longline, killer whale, sperm whale, bio-logging, human behavioural ecology passive acoustic monitoring

Behavioural ecology of fishermen and odontocetes in a depredation context on the Patagonian toothfish longliners around Crozet and Kerguelen islands

Many marine predator species feed on fish caught by fishers directly from the fishing gear. Known as depredation this interaction issue has substantial socio-economic consequences for fishermen and conservation implications for the wildlife. Costs for fishers include damages to the fishing gear and increased fishing effort to complete quotas. For marine predators, depredation increases risks of mortality (lethal retaliation from fishers or bycatch on the gear). Longline fisheries are the most impacted worldwide, primarily by odontocetes (toothed whales) depredation, urging the need for mitigation solutions to be developed. Most of studies assessing depredation have primarily relied on surface observation data, thus the way odontocetes interact with longlines underwater remains unclear. Besides, the way fishermen respond to depredation during fishing operations, or can influence their detectability to odontocetes, have been poorly investigated. This thesis therefore aimed at investigating these aspects through a passive acoustic monitoring, bio-logging and human ecology approaches, focusing on the French Patagonian toothfish (Dissostichus eleginoides) longline fisheries impacted by killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus). Firstly, this thesis reveals that captains behave as optimal foragers but with different personal perception of competition and fishing fulfilment. Some captains would thus be more likely to stay within a patch or to haul closest longline even in presence of competition, suggesting these captains would show higher interaction rates. Additionally, the propagation of vessels' acoustics varied depending on the type of manoeuvre (e.g. going backward vs. forward). The way captains use their vessels to navigate may therefore influence their detectability and so their depredation level. Secondly, loggers deployed on both the longlines (accelerometers) and odontocetes (GPS-TDR) revealed that killer whales and sperm whales are able to depredate on longlines while soaking on the seafloor. These observations suggest, therefore, that odontocetes can localise fishing activity before the hauling, which could be partially explained by specific acoustic signatures recorded during the setting process. Altogether, the results of the thesis suggest that depredation rates on demersal longlines are most likely underestimated. The thesis also brings some important insights for mitigation measures, suggesting that countermeasures should start from setting to hauling.

RESUME Ecologie comportementale des pêcheurs et odontocètes dans un contexte de déprédation sur les palangriers à la légine australe autour des îles Crozet et Kerguelen

De nombreux prédateurs marins se nourrissent directement des prises des pêcheurs. Ces interactions, définies comme de la déprédation, engendrent des conséquences socio-économiques considérables pour les pêcheurs ainsi que des implications de conservation pour la faune sauvage. D'un côté, la déprédation endommage le matériel et augmente l'effort de pêche pour atteindre les quotas. D'un autre côté, la déprédation augmente le risque de mortalité des prédateurs marins (prise accidentelle ou rétorsion létale par les pécheurs). La pêcherie à la palangre est la plus impactée par la déprédation, principalement par les odontocètes, ce qui incite à trouver des solutions. La majorité des études se concentrant sur la déprédation s'est principalement basée sur des observations en surface, de ce fait la manière dont les prédateurs retirent les poissons sur les lignes reste confuse. Par ailleurs, l'impact de la déprédation sur le comportement des pêcheurs ainsi que les facteurs expliquant leur détectabilité n'ont reçu que peu d'intérêt. L'objectif de cette thèse est donc d'étudier ces problématiques par un suivi acoustique, une utilisation de balises et une approche en écologie comportementale humaine, en se concentrant sur la pêcherie palangrière française ciblant la légine australe (Dissostichus eleginoides) impactée par la déprédation des orques (Orcinus orca) et des cachalots (Physeter macrocephalus). Les capitaines ont été décrits comme recherchant leur ressource selon la théorie de « l'optimal foraging », mais avec des perceptions de la compétition et du succès de pêche qui divergent. Certains capitaines seraient ainsi plus enclins à remonter les palangres au plus proche et à rester sur une zone, même en présence de compétition, augmentant alors le risque d'interaction. L'acoustique des navires a révélé que certaines manoeuvres (marche arrière par exemple) propagent différemment sous l'eau. La manière dont les capitaines manoeuvrent leur palangrier influencerait ainsi leur détectabilité et donc leur risque d'interaction avec les prédateurs. D'autre part, l'utilisation de capteurs sur les palangres et les animaux a révélé que les orques et les cachalots sont capables de déprédater sur les palangres posées sur le fond marin. Ces observations laissent à penser que les odontocètes sont en mesure de localiser l'activité de pêche bien avant la remontée de la ligne, ce qui pourrait être expliqué par une signature acoustique spécifique du déploiement de la ligne. L'ensemble des résultats de cette thèse suggère que la déprédation sur les palangres démersales est très probablement sous-estimée. Cette thèse apporte également des éléments importants pour la lutte contre la déprédation, en montrant la nécessité de protéger les palangres dans l'intégralité du processus de pêche.

Mots clés : déprédation, palangre démersale, orques, cachalots, bio-logging, écologie comportementale humaine, acoustique passive

ACKNOWLEDGMENT / REMERCIEMENTS

First of all, I would like to thank sincerely Dr. Aaron Thode, Dr. Simon Northridge and Pr. Guy Duhamel for accepting to review my thesis such as Pr. Vincent Ridoux and Dr. Charlotte Curé for agreeing to join the defence committee. I am very grateful for the time you granted to my thesis and I am honoured to garner your judgments.

Je tiens à remercier tout particulièrement Christophe Guinet. Merci de m'avoir ouvert les portes de ton équipe lors de mon stage de licence 3 et de m'avoir fait découvrir le monde de la recherche. Je te suis très reconnaissant pour tout ce que tu m'as apporté pendant mon cursus et de la confiance que tu m'as accordé pendant cette thèse.

Un immense merci aussi à Julien Bonnel, notamment de m'avoir accepté dans ton équipe pour mon stage de master 2 en vue de cette thèse. Merci pour tout ce que tu m'as appris, pour toute l'expertise que tu m'apportes et de me pousser toujours un peu plus dans la rigueur scientifique ! Thanks a lot to John Arnould, for contributing to this cotutelle, it was a great experience to come in Australia and to share about our projects.

Je remercie aussi énormément Paul Tixier. Merci pour ton aide précieuse penadant ma thèse, pour tes conseils de terrain, pour ton accueil en Australie et ton soutien lors de l'écriture de ce manuscrit. Je suis très content d'avoir pu contribuer un peu plus à ce projet initié lors de ta thèse ! Je remercie également les directeurs de Chizé qui se sont succédés pendant mes passages au laboratoire: Vincent Bretagnolle, Xavier Bonnet et Christophe Guinet, pour leur accueil et de faire en sorte que le labo tourne si bien. Merci à l'équipe administrative pour votre travail afin que notre vie au labo se passe bien ainsi que votre aide dans mes gestion de logistique pour mes missions sur les palangriers. Merci aux cuisiniers hors-pair pour ce soutien aussi bien physiologique que psychologique du quotidien. Merci aux chercheurs de partager leur expérience et d'apporter tant aux étudiants.

Un grand merci à l'équipe de l'ENSTA Bretagne aussi pour votre accueille lors de mes venues à Brest. Merci à Flore Samaran pour son aide précieuse sur toute cette logistique terrain ! Et je te remercie pour la confiance que tu m'accordes pour la suite de cette aventure.

Merci à Paco Bustamante, Jennifer de La Corte Gomez, Isabelle Hirsch et Virginie Bernard pour leur travaille et suivi des doctorants de l'Université de La Rochelle.

I also thank Deakin University and the Marine and Freshwater Discovery Centre of Queenscliff for welcoming me. Thank you Rheanne Hogema for all your help with the Australian administration.

Un grand merci au MNHN avec qui nous collaborons, sans le partage de leurs données PECHKER une bonne partie de cette thèse n'aurait pu se faire. Un merci particulier à Nico Gasco pour tous les échanges constructifs que nous avons eu et son aide lors de mon expérience improvisée de COPEC.

Je remercie aussi les TAAFs et la réserve naturelle pour leur contribution dans l'organisation du terrain sur les palangriers. Merci beaucoup à Thibaut Tellier pour toute l'aide apportée sur la gestion logistique et administrative de mes missions.

Merci aussi aux contrôleurs de pêche pour leurs efforts pour le projet, avec une mention spéciale pour Gabriel Devique et Hugues Vermande d'avoir partagé leur expérience et de m'avoir appris le métier.

Un très grand merci aux armements et leur fondation, ainsi qu'à la SARPC sans qui mes embarquements n'auraient pu se réaliser, merci pour votre collaboration.

Je remercie aussi profondément les capitaines (Georges, Julien, Alain) ainsi que leurs équipages (mes excuses de ne pouvoir tous vous citer mais je n'oublie personne) pour votre accueille sur vos navires, pour votre aide dans mes manips et le partage de vos expériences en mer. J'ai pu expérimenter de nombreux soucis techniques mais pour un marin il n'y a pas de problème que des solutions, notamment grâce à vos mécaniciens exceptionnels. Mes embarquements à vos côtés furent riches autant professionnellement que personnellement.

I would like to warmly thank also people who plaid an important role during my path to this thesis, with all these great experiences and good memories. Большое спасибо Olga Filatova and Ivan Fedutin, for this amazing first field experience in Kamchatka, and all the work we have been able to conduct together afterwards, I have learned so much with you! Trugarez Jean-Luc Jung, merci d'avoir mis en place cette collaboration avec Olga lors de ma recherche de visa et de m'avoir ensuite fait confiance dans mes premiers pas de généticiens, ce fut une expérience très riche ! Muito obrigado or Takk fyrir Filipa Samarra for welcoming me in your (boom) team in Iceland and in Saint Andrews with Olga, thank you for all you taught me with this unrestricted good mood! Pendant cette thèse j'ai pas mal crapahuté et donc fait de nombreuses et belles rencontres, merci à tous les amis de récentes ou longues dates, ayant partagé des moments de cette aventure ou m'ayant soutenu (même à distance) ! Je ne citerai pas de noms car vous êtes beaucoup trop nombreux mais… … merci à la précieuse team du Black Pearl, pour votre soutien inconditionnel, si on décernait aussi des thèses en amitié, on serait déjà tous docteurs ! Merci aux copains de Verlinghem, toujours un plaisir de vous voir lors de mes retours aux sources. Thanks to my Scottish mates for what you brought me before my PhD and it is always nice to see you at some conferences! Merci aux copains brestois pour l'accueil chaleureux à chaque fois que je suis de passage, sans oublié Cookie bien sûr. Thanks to my flatmates of Barwon Heads, such a great welcome, and to my other friends in Australia! Et finalement merci à tous les copains de Chizé et La Rochelle, mes colocs du dortoir du labo, de Villefollet, d'Olbreuse et de bureau/bateau… une belle équipe qui rend ce quotidien de travail des plus agréables.

Je finirais ces remerciements par les plus importants sentimentalement, merci à toute ma famille pour son soutien constant! Merci à mes parents de m'avoir donné le goût du voyage et de la découverte, merci de me soutenir dans mes choix et de croire en moi ! Merci à mon frère et ma belle-soeur pour le bonheur qu'ils dégagent ! Merci mamie pour tes calembours et calembredaines en tous genres, véritable source d'inspiration, et merci à mon papi pour avoir toujours porté de l'intérêt à ma vocation, cette thèse te restera inachevée et je te la dédie.

To my grandfather, à mon papy Milo…

FOREWORD

This thesis was conducted within a cotutelle agreement between the Université de La Rochelle (Ecole Doctorale EUCLIDE) and of Deakin University (School of Life and Environmental Sciences, Melbourne), under the supervision of Dr. Christophe Guinet (CEBC, UMR 7372 CNRS-ULR), Dr. John Arnould (Deakin University) and Dr. Julien Bonnel (ENSTA Bretagne, Brest). The PhD was lead during 22 months at the CEBC, 5 months at the ENSTA Bretagne, 3 months at Deakin University and 6 months on mission on-board longliners

The thesis has been funding by a departmental and regional grant (Deux-Sèvres and Poitou-Charentes). Financial support has also been provided by the ANR ORCADEPRED, the This PhD is part of a broader project research project named OrcaDepred, lead by Dr.

Christophe Guinet and involving 8 partner-institutions (CEBC-CNRS, MNHN, ENSTA Bretagne, UBO, IFREMER, UMI 2958 GT-CNRS, AMURE, IRD UMR MARBEC) as well as one industrial provider (SATIM). Within this framework, this thesis has contributed to scientific communications under various formats.

1) Peer review articles:

Articles drafted as the first author during this PhD and constituting the core of the thesis:

 Richard, G., C. Guinet, J. Bonnel, N. Gasco and P. Tixier. 2017. Do commercial fisheries display optimal foraging? The case of longline fishers in competition with odontocetes.

Canadian Journal of Fisheries andAquatic Sciences. doi:10.1139/cjfas-2016-0498. (Chapter 2)

 Richard, G., C. Guinet, J. Bonnel, N. Gasco and P. Tixier. Evidence of deep-sea interactions between toothed whales and longlines bring new insights to fisheries management. under review Ambio (Chapter 4) Articles drafted as a co-author, contributing to the thesis:  Janc, A., G. Richard, C. Guinet, J. P. Y. Arnould, M. C. Villanueva, G. Duhamel, N. Gasco and P. Tixier. 2018. How do fishing practices influence sperm whale (Physeter macrocephalus) depredation on demersal longline fisheries? Fisheries Research 206: 14-26. doi:10.1016/j.fishres.2018.04.019. (Appendix 4) this article was realized as part of a collaborative work with my colleague Anaïs Janc, I have contributed to the analyses process and I have helped to the manuscript's drafting. [START_REF] Lennert | At the cutting edge of the future: Unravelling depredation, behaviour and movement of killer whales in the act of flexible management regimes in Arctic Greenland[END_REF]. At the cutting edge of the future: Unravelling depredation, behaviour and movement of killer whales in the act of flexible management regimes in Arctic Greenland. Ocean & Coastal Management 148: 272-281. doi:10.1016/j.ocecoaman.2017.08.016. (Appendix 7) this article was drafted apart from my PhD with Dr. Ann Eileen Lennert, and I have contributed to the design of the study and provided the review on killer whales ecology and depredation issue.

As co-author, not included in the thesis:  Poster at the 6th Bio-logging Science Symposium in Konstanz (Germany), September 2017: "how to assess depredation from longlines with accelerometers?"  Talk at the French workshop SERENADE organized at the ENSTA Bretagne, Brest from the 15 th to the 18 th of October 2018.

Science popularization:

 Tutoring Master's students of Poitiers university (2 weeks,French and English), September

ANTHROPOCENE

Humans are exceptional ecosystem engineers. As all species, we have attempted to enhance our environment to survive, but we distinguished ourselves by the use of tools and technologies that we transmitted through culture over generations (Smith 2007, Ellis and[START_REF] Ellis | Putting people in the map: anthropogenic biomes of the world[END_REF]. The shift that we created with the natured occurred with the learning process to ignite and master fire during the Palaeolithic [START_REF] Marlowe | Hunter-gatherers and human evolution[END_REF][START_REF] Smith | BEHAVIOR: The Ultimate Ecosystem Engineers[END_REF][START_REF] Ellis | Putting people in the map: anthropogenic biomes of the world[END_REF][START_REF] Glikson | Fire and human evolution: The deep-time blueprints of the Anthropocene[END_REF]. Fire allowed an increase of the hunting pressure and the first landscape modification through controlled burning of vegetation [START_REF] Marlowe | Hunter-gatherers and human evolution[END_REF][START_REF] Smith | BEHAVIOR: The Ultimate Ecosystem Engineers[END_REF][START_REF] Archibald | Evolution of human-driven fire regimes in Africa[END_REF][START_REF] Glikson | Fire and human evolution: The deep-time blueprints of the Anthropocene[END_REF]. Another profound impact of humans on fauna was the domestication of animals. First domestication occurred with the dog between 32 000 and 15 000 years ago (e.g. [START_REF] Freedman | Genome Sequencing Highlights the Dynamic Early History of Dogs[END_REF][START_REF] Germonpré | Large canids at the Gravettian Předmostí site, the Czech Republic: The mandible[END_REF], 2017[START_REF] Frantz | Genomic and archaeological evidence suggest a dual origin of domestic dogs[END_REF], and was followed by the domestication of other animals but also plants, 12 000 -10 000 years ago [START_REF] Pluciennik | Review Reviewed Work(s): The Emergence of Agriculture by Bruce D. Smith[END_REF][START_REF] Leach | Human Domestication Reconsidered[END_REF][START_REF] Steffen | The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature[END_REF]). These domestications led to the Neolithic Revolution with a settlement of the human population and the development of early agriculture. This transition started to shape permanently ecosystems, by clearing forest and irrigating fields, and might have led to the first small increases in atmospheric carbon dioxide and methane concentrations between 8000 and 5000 years ago [START_REF] Ruddiman | The Anthropogenic Greenhouse Era Began Thousands of Years Ago[END_REF][START_REF] Steffen | The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature[END_REF].

Preindustrial human societies have, thus, influenced their local environment with these new artificial habitats, but it was with the Industrial Revolution in Europe (end 18 th -beginning 19 th centuries) that human activity started to impact heavily on ecosystems [START_REF] Steffen | The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature[END_REF], 2011[START_REF] Ellis | Anthropogenic transformation of the terrestrial biosphere[END_REF][START_REF] Lewis | Defining the Anthropocene[END_REF]. The industrial revolution was first and foremost an energy revolution and constitutes the starting point of a new drastic shift between human and nature. Human societies used to rely on energy captured from ongoing flows (wind, water, animals, plants and trees) and changed in the 1800's to fossil fuels (coal, oil and gas), increasing the carbon dioxide and methane emissions. However, the transition to these high-energy fuels brought new technologies improving then the mobility and revolutionizing the agriculture.

These innovations have been constantly improving human health condition and, thus, resulting to an important growth of the human population, with a demographic explosion in the second half of the 20 th century [START_REF] Steffen | The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature[END_REF][START_REF] Steffen | The Anthropocene: conceptual and historical perspectives[END_REF][START_REF] Steffen | The trajectory of the Anthropocene: The Great Acceleration[END_REF]. It is now considered that no ecosystem on Earth remains free of human influence (Vitousek et al. 1997, Ellis and[START_REF] Ellis | Putting people in the map: anthropogenic biomes of the world[END_REF].

Overexploitation of resources and pollution due to Human activity is now altering the atmosphere, the hydrosphere, the lithosphere and the biosphere [START_REF] Vitousek | Human Domination of Earth[END_REF][START_REF] Vörösmarty | Anthropogenic Disturbance of the Terrestrial Water Cycle[END_REF][START_REF] Crutzen | Geology of mankind TheAnthropocene[END_REF][START_REF] Foley | Global Consequences of Land Use[END_REF][START_REF] Ellis | Putting people in the map: anthropogenic biomes of the world[END_REF][START_REF] Rockström | Planetary Boundaries: Exploring the Safe Operating Space for Humanity[END_REF][START_REF] Ellis | Anthropogenic transformation of the terrestrial biosphere[END_REF]). These rapid and significant shifts of the global environment due to human activity have led to the definition of a new geological era: the Anthropocene [START_REF] Crutzen | Geology of mankind TheAnthropocene[END_REF][START_REF] Steffen | The Anthropocene: Are Humans Now Overwhelming the Great Forces of Nature[END_REF][START_REF] Zalasiewicz | The Anthropocene: a new epoch of geological time?[END_REF][START_REF] Lewis | Defining the Anthropocene[END_REF]. This human domination over the ecosystems questions also the definition of "biomes" in ecology commonly considered as "natural ecosystems with humans disturbing them", and some specialists suggest rather talking about "anthromes" defined as "human systems, with natural ecosystems embedded within them" [START_REF] Ellis | Putting people in the map: anthropogenic biomes of the world[END_REF]. Within this context, conflicts with wildlife are unavoidable.

DEFINITION OF THE CONSERVATION CONFLICTS

Human-wildlife conflict has been defined as an action by humans or wildlife with an adverse effect on the other [START_REF] Conover | Resolving Human-Wildlife Conflicts: The Science of Wildlife Damage Management[END_REF]. Their causes have been attributed mostly to the loss and fragmentation of existing habitats and to the increase of the competition for resources between human and wildlife. The first conflicts occurred during the Palaeolithic while first hominids were both hunted and hunters (Lee-Thorp et al. 2000, Dickman and[START_REF] Dickman | Money, Myths and Man-Eaters: Complexities of Human-Wildlife Conflict[END_REF].

Although humans are not systematically hunted anymore, attacks still occurred and can be considered the most direct damage for humans, even though they remain rare [START_REF] Löe | Large Carnivores and Human Safety: A Review[END_REF]Röskaft 2004, Dickman and[START_REF] Dickman | Money, Myths and Man-Eaters: Complexities of Human-Wildlife Conflict[END_REF]. With the advent of the agriculture and human settlements, thus depredation developed as another major human-wildlife conflict [START_REF] Sillero-Zubiri | Interactions between carnivores and local communities: Conflict or co-existence[END_REF][START_REF] Woodroffe | People and wildlife: conflict or coexistence?[END_REF][START_REF] Mcmanus | Dead or alive? Comparing costs and benefits of lethal and non-lethal human-wildlife conflict mitigation on livestock farms[END_REF][START_REF] Dickman | Money, Myths and Man-Eaters: Complexities of Human-Wildlife Conflict[END_REF].

Depredation is the feeding behaviour adopted by wild animals upon food either produced, farmed or captured by humans. The loss of crops or livestock to wildlife has significant consequences on people's livelihoods and agricultural security (Barua et al. 2013, Dickman and[START_REF] Dickman | Money, Myths and Man-Eaters: Complexities of Human-Wildlife Conflict[END_REF]. Historically, humans primarily responded to these conflict by lethal retaliation towards the depredating species [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF]. With the growth of human populations, their spreading around the world and the increase of their activity, impacts of lethal retaliation on the wildlife have become increasingly severe. These responses, paired with growing anthropic pressure upon ecosystems, have caused a massive loss of biodiversity, leading some specialists to talk about "defaunation" as an analogy of "deforestation" [START_REF] Dirzo | Defaunation in the Anthropocene[END_REF]). We may be assisting the Earth's sixth mass extinction [START_REF] Chapin | Consequences of changing biodiversity[END_REF][START_REF] Barnosky | Has the Earth's sixth mass extinction already arrived?[END_REF][START_REF] Dirzo | Defaunation in the Anthropocene[END_REF].

The mass extinction of biodiversity combined with an increase resource depletion led to a rise of awareness within human societies in the 70s with the club of Rome and their report "The Limits to Growth" (1972). Indeed, biodiversity is an essential provider of ecosystem goods but also services, contributing to societies' economy (Millennium Ecosystem Assessment 2005). With this trigger alarm, a new field of research arose: the conservation biology, defined as the "application of science to conservation problems that addresses the biology of species, communities, and ecosystems that are perturbed, either directly or indirectly, by human activities or other agents" [START_REF] Soulé | What Is Conservation Biology?[END_REF]. One purpose of this new field was to address the issues of human-wildlife conflicts in order to mitigate the anthropic pressure upon the biodiversity. Thus, biologists in conservation mostly urge decision toward the preservation of wildlife. However, within a conflict context, these decisions might be at the expense of human activities and therefore tend to generate a "human-human" conflicts rather than "humanwildlife" conflict. Two types of conflicts are then distinguished: (i) "human-wildlife conflicts defined as the direct damages between humans and other species) and (ii) "conservation conflicts" referring to "situations that occur when two or more parties with strongly held opinions clash over conservation objectives and when one party is perceived to assert its interests at the expense of another" [START_REF] Woodroffe | People and wildlife: conflict or coexistence?[END_REF][START_REF] White | Developing an integrated conceptual framework to understand biodiversity conflicts[END_REF][START_REF] Young | The emergence of biodiversity conflicts from biodiversity impacts: characteristics and management strategies[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF].

HOW TO SOLVE CONSERVATION CONFLICTS?

A solved "conflict" is a situation where both parties can coexist, and within a conservation context it also includes a decrease of the human-wildlife impact [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sarrazin | Evolution in the Anthropocene[END_REF]. A clear identification of the actors involved in a conflict along with their respective interests is therefore necessary to achieve right management and to tend toward a win-win situation [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Young | The emergence of biodiversity conflicts from biodiversity impacts: characteristics and management strategies[END_REF][START_REF] Colyvan | The conservation game[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. As such, a third party is often requested to plan a sustainable management of the conflict as long as they maintain transparency in their assumptions and inferences to be trusted by the actors involved in the conflict [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF]. The role of scientists is first to measure the perceptions of conflicts as well as the behaviours of the involved actors either humans or animals [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Blackwell | No single solution: application of behavioural principles in mitigating human-wildlife conflict[END_REF]. As part of this process, behavioural ecology is a field of importance as it allows for a better understanding of the human impacts on wildlife. This field of ecology aims indeed at investigating the evolutionary basis for animal behaviour in response to ecological pressures. When environments are shaped by human activity, this approach can target how human activity influences the behaviours of wild animals and the consequences on the adaptability and survival of the wildlife populations. Besides, a good understanding of animal behaviour, both in presence and absence of human activity, may help to develop possible trade-offs involving alternative options for the actors to be used as techniques avoiding lethal retaliation [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Sarrazin | Evolution in the Anthropocene[END_REF][START_REF] Blackwell | No single solution: application of behavioural principles in mitigating human-wildlife conflict[END_REF]. The implementation of solutions must always be followed by monitoring to judge the effectiveness of interventions. Scientists have to evaluate whether the shared objectives between the wildlife conservation and human welfare have been reached [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF].

The steps for a good management of a conservation conflict seem in theory quite achievable. However, these conflicts are in practice very complex as they require balance of the interests between all actors and might be in some case unreachable [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Dickman | Money, Myths and Man-Eaters: Complexities of Human-Wildlife Conflict[END_REF]. In addition, the complexities of ecosystems where conflicts occur paired with the lack of knowledge on animals' behaviour often result in a poor understanding of the conservation priority. This may be especially true for ecosystems which are not directly shaped by humans, such as in agriculture where land modification allows the control and understanding of most ecosystem parameters. This is the case for marine environments, a complex ecosystem hardly accessible to humans [START_REF] Lewison | Embracing Complexity and Complexity-Awareness in Marine Megafauna Conservation and Research[END_REF].

However, although humans cannot settle and thus shape directly the marine environments, they nevertheless succeeded to exploit aquatic ecosystems all over human evolution.

MARINE DEPREDATION

CONTEXT OF MARINE DEPREDATION: FISHING ACTIVITY

The Earth's surface is 71 % covered by water and 96.5% of this water is held within the oceans. Marine ecosystems are predominant on our planet and all of them have been impacted by anthropic activities, either indirectly through human pollution or directly through resources exploitation [START_REF] Halpern | A Global Map of Human Impact on Marine Ecosystems[END_REF]. The exploitation of the oceans started as early as the Palaeolithic with the hunter-gatherer foraging on fish and molluscs along the coast [START_REF] Erlandson | The Archaeology of Aquatic Adaptations: Paradigms for a New Millennium[END_REF][START_REF] Erlandson | Shellfish Feeders, Carrion Eaters, and the Archaeology of Aquatic Adaptations[END_REF][START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF]. Through human evolution and techniques developments, humans have then been able to go further away from the coast and to exploit offshore ecosystems, increasing their fishing pressure on target species and on their supporting ecosystems [START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF]. Nowadays, all humans fisheries capture more than 90 million of tonnes of fish per year, including fish not targeted but kept on board (bycatch), with more than 30% considered as overfished (FAO 2016). These estimations do not account for discards, i.e. fish and other marine life that are thrown overboard, estimated around 10% of the global total catches, which represents a hidden impact not marginal [START_REF] Kelleher | Discards in the world's marine fisheries: an update. Food and Agriculture Organization of the United Nations[END_REF][START_REF] Zeller | Global marine fisheries discards: A synthesis of reconstructed data[END_REF]).

However, even by assessing these discards, the total catch is still underestimated since Illegal, Unregulated and Unreported (IUU) fishing is not accounted. With an estimated 18% of the global catch in the early 21 st century, IUU fishing is likely to greatly contribute to the current overexploitation of fish stocks [START_REF] Agnew | Estimating the worldwide extent of illegal fishing[END_REF]). Thus, through human evolution, fisheries have led to important depletion of species, questioning the sustainability of this activity [START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF][START_REF] Pauly | Towards sustainability in world fisheries[END_REF]. However, despite the collapse of some fishing stocks during the last decades, most of fishing efforts have kept growing [START_REF] Pauly | Towards sustainability in world fisheries[END_REF], FAO 2016).

Fisheries affect marine wildlife and ecosystems in various ways. The first direct impact of fishing is the reduction of the target species' abundance, threatening the stocks with extinction through overexploitation [START_REF] Casey | Near Extinction of a Large, Widely Distributed Fish[END_REF][START_REF] Pauly | Towards sustainability in world fisheries[END_REF]. Removing species, or at least part of a population, modifies the natural balance of ecosystems through a reduction of the number and length of pathways within food webs [START_REF] Pauly | Fishing Down Marine Food Webs[END_REF][START_REF] Pauly | Towards sustainability in world fisheries[END_REF]. Fisheries can generate both top-down and bottom up effects. On the one hand, top-down effects occur when fisheries primarily target large individuals from upper trophic levels, this reduces predatory pressure on prey, which therefore leads to overloaded populations in lower trophic levels of the ecosystem, subsequently causing deleterious effects [START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF][START_REF] Daskalov | Overfishing drives a trophic cascade in the Black Sea[END_REF][START_REF] Pauly | Towards sustainability in world fisheries[END_REF][START_REF] Lynam | Interaction between top-down and bottom-up control in marine food webs[END_REF]. On the other hand, bottom-up effects occur when fisheries exploit a resource used by marine predators. The marine top predators may either change their diet and thus introduce a new predatory pressure on other species disrupting the ecosystem balance [START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF][START_REF] Lynam | Interaction between top-down and bottom-up control in marine food webs[END_REF], or enter in direct competition with fisheries [START_REF] Trites | Competition between fisheries and marine mammals for prey and primary production in the Pacific Ocean[END_REF][START_REF] Demaster | Predation and competition: the impact of fishereis on marine-mammal populations over the next one hundred years[END_REF][START_REF] Matthiopoulos | Getting beneath the surface of marine mammalfisheries competition[END_REF]. Competitive interaction between top marine predators and fisheries could result in a negative impact on marine predators populations due to marine resources depletion [START_REF] Trites | Competition between fisheries and marine mammals for prey and primary production in the Pacific Ocean[END_REF][START_REF] Yodzis | Must top predators be culled for the sake of fisheries?[END_REF][START_REF] Demaster | Predation and competition: the impact of fishereis on marine-mammal populations over the next one hundred years[END_REF][START_REF] Matthiopoulos | Getting beneath the surface of marine mammalfisheries competition[END_REF]. However, competition also leads to operational interactions defined when marine predators come into physical contact with fishing gear to withdraw the catch [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | Bycatch of Marine Mammals in U.S. and Global Fisheries: Bycatch of Marine Mammals[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]).

These interactions happen when there is co-occurrence of foraging activity or when marine predators are attracted by fishing activity. This latter behaviour has been defined as depredation, since the predators feed upon a resource captured by humans [START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. With the extension of fishing activity over the last decades, direct interactions between human fisheries and marine top predators have been increasing, raising new conservation conflicts [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Read | Bycatch of Marine Mammals in U.S. and Global Fisheries: Bycatch of Marine Mammals[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]).

DEFINITION OF THE MARINE DEPREDATION CONFLICTS

Depredation on fisheries by marine predator is a type of situation where human activity and wildlife behaviour have both an adverse effect on the other, defining thus a "human-wildlife conflict" [START_REF] Conover | Resolving Human-Wildlife Conflicts: The Science of Wildlife Damage Management[END_REF]). On the one hand, depredation has substantial socio-economic consequences for fishermen. Indeed, this behaviour by marine predators is characterised by the removal of the whole capture or the partial consumption of the fish, decreasing the fish economic value and damaging the fishing gear at the same time [START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. Additionally to these induced costs, fishermen increase their fishing effort to complete their quota or to avoid depredation, increasing expenses due to fuel consumption, extra food and crew salaries [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Peterson | Whale interactions with Alaskan sablefish and Pacific halibut fisheries: surveying fishermen perception, changing fishing practices and mitigation[END_REF][START_REF] Peterson | Killer Whale Depredation and Associated Costs to Alaskan Sablefish, Pacific Halibut and Greenland Turbot Longliners[END_REF], Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. On the other hand, depredation for marine predators increases risks of mortality by the use of lethal retaliation by fishermen [START_REF] Yano | Behavior of Killer Whales Orcinus orca during Longline Fishery Interactions in the Southeastern Bering Sea and Adjacent Waters[END_REF][START_REF] Woodroffe | People and wildlife: conflict or coexistence?[END_REF][START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF], or by a greater risk of bycatch, especially in gillnets [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | Bycatch of Marine Mammals in U.S. and Global Fisheries: Bycatch of Marine Mammals[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Reeves | Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011[END_REF]. For instance, it has been observed that at least 75% of odontocetes, 64% of mysticetes, 66% of pinnipeds, and all sirenians and marine mustelids species have been recorded as gillnet bycatch since the 1990's [START_REF] Reeves | Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011[END_REF]. Depredation has been observed on all fisheries and by a broad range of large marine vertebrates [START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. However, most of these conflicts involved the longline fisheries, with a growing cases since the 1950s [START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF], and have involved mainly odontocetes species [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Reeves | Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Indeed, at least 31 species of odontocetes have been reported to interact (either depredation or bycatch) with longline fisheries against 15 species of pinnipeds, 6 species of mysticetes, and 2 species of sirenians [START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Increasing longline depredation may be firstly explained by the large expansion of this fishing gear worldwide in the last 70 years. Indeed, longlines are more selective in catching targeted fish species than most of the other fishing gears and have thus been favoured in many fisheries [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. Secondly, longline is an easy target for marine predators as baits and fish are freely accessible. Indeed, longlines consist of snoods connecting unprotected hooks at intervals along a mainline. The longline fishing process is composed of three steps (Figure 1-1): (i) the setting, which is the phase when hooks are baited and longlines are deployed at sea, and which generally lasts from less than an hour to a few hours; (ii) the soaking, which is the phase when fish is caught as the longline is left at sea with no boat activity, and which lasts from a few hours to a few days depending on fisheries; (iii) the hauling, which is the phase when longlines with the catch are retrieved aboard boats, and which, for a given longline, generally lasts longer than the setting phase. Longlines can either be deployed within the water column close to the surface, i.e. pelagic longlines, or on the seafloor, i.e. demersal longlines. Depredation has been mostly described to occurr during hauling of the longlines for both types [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF], Guinet et al. 2015[START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF], Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF] and also during the soaking time for pelagic longlines [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Rosa | Killer whale (Orcinus orca) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Forney | What's the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries: Patterns of cetacean bycatch and depredation in Hawaii[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF][START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF]. As longlines may increase the availability of food for top marine predators, the development of this fishing technique has caused substantial ecological chain reactions. For marine predators, foraging on fish already caught requires very low capture effort and therefore increases energetic intake. This may result in a positive influence on the survival and reproduction of individuals in some populations, even though the risk of mortality by entanglement on longlines remains high [START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF], Tixier et al. 2017). As a result marine predators are likely to favour this behaviour over natural foraging, subsequently increasing the risk of a habituation to fisheries and/or a switch of their natural diet and so their foraging strategies, and also their natural spatial distribution [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF]. In turn, these behavioural modifications longline depredation may threaten the prey-predatory balance established in the ecosystem by increasing the predatory pressure on the targeted fish, threatening the fish stocks of depletion.

All these ecological impacts paired with bycatch on longlines of emblematic species have created a negative public perception of longline fishing and raised conservation programs to protect either marine predators and fish stocks [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF]. As most of these actions are more likely to reduce fishing activity, the "human-wildlife conflicts" should be better considered as a "human-human conflicts" [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. This conservation conflicts requires solution to reach a situation where marine wildlife is not threaten and fisheries are sustainable [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF].

HOW TO SOLVE THE MARINE DEPREDATION CONFLICTS?

Identifying solutions to marine depredation involves developing mitigation measures with limited costs for all parties of the conflict. On one hand, preventing marine predators from interacting with fisheries would suppress/decrease the mortality risk of these animals as well as all the ecological consequences of depredation on the ecosystem. Besides, it would allow reducing the fishing effort to catch the same amount of fish, and thus decrease fishing pressure on the environment as well as improving fisheries sustainability. However, to find the appropriate mitigation is challenging since preventing marine predators from interacting with fishing gear should not decrease the fishing efficiency. Solutions are then mostly dependant on the fish targeted, the fishing gear used and the depredating species. The first step toward a resolution of the conflict is then a good understanding of these three components. Thus, as previously discussed, scientists have an important role to play as they can study interaction between marine predators and fisheries, by assessing behaviour of both actors, and then implement experimental solutions and monitor their efficiency.

Depredation is a conflict that can theoretically be mitigated using two non-lethal approaches either operational or technological. Firstly, a better understanding of fishing practice and strategy with their causes or consequences on depredation rates may lead to relatively easy mitigation solution to implement with limited cost for fisheries. Indeed, through this operational approach scientists can make recommendation for fishermen on the use of their fishing gears and propose more efficient strategies to limit marine predators' access to catch and bait [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. For instance, the hauling speed or the setting length or deployment depth of longlines may gain time and reduce the probability of interaction [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF], 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF].

Another improvement of fishing strategy involves a better spatio-temporal use of the fishing area by targeting grounds where and when the probability of encounter with marine predators is the lowest [START_REF] Dunn | Empirical move-on rules to inform fishing strategies: a New England case study[END_REF], Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. This approach requires assessing the marine predators' ecology to know if the predators have some preferential foraging areas/seasons within the fishing zone, or whether they have diel feeding variations or also if they might be migratory species. However, it is likely to have strong spatiotemporal overlaps between the fishing activity and the predators' foraging, as both wildlife and humans may be attracted by high fish densities grounds. Thus, changes of fishing strategies may sometimes decrease fishing success as well as increase cost for fisheries. For instance, alternative fishing practices might have a human impact when requiring fishermen to work either faster or longer or in worst condition. As a result, these changes in fishing strategies could reduce depredation levels but they rarely solve completely the conflict.

Additional approaches are then needed, such as methods to protect the catch either by repelling the predators or by avoiding detection of the fishing activity or by using a physical protection of the gear to avoid access to the fish. These technological approaches seem in theory the best ways to avoid depredation. Nevertheless, it is in practice hard to implement since it faces a lot of constraints to meet a trade-off between being economically viable for fishermen and harmless for marine predators. Behavioural ecology has an important role to play as it is essential to know how fishing activity is detected by the predators and how the depredation occurs to target the most appropriate solution. For the first purpose, it is essential to assess how marine predators perceive their environment and localise they prey. For instance, using a sensory ecological approach, some studies have found that shark are sensitive to chemical, magnetic, electropositive rare earth metal and electrical repellents [START_REF] Gilman | Shark interactions in pelagic longline fisheries[END_REF]). As for odontocetes some studies have focused on developing the use of acoustic deterrence, since they primarily rely on acoustic to forage. Although it seems impossible to deter cetaceans by acoustics, using pingers on gillnets to signal their presence to dolphins and porpoises have shown promising results in decreasing odontocete bycatch [START_REF] Reeves | Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011[END_REF][START_REF] Maccarrone | Economic Assessment of Dolphin Depredation Damages and Pinger Use in Artisanal Fisheries in the Archipelago of Egadi Islands[END_REF]. However, for fishing gears where fish is freely accessible, the interest is not to increase fishing gear detection with pingers but to repel the animals with acoustic harassment [START_REF] Arangio | Minimising whale depredation on longline fishing[END_REF], Tixier et al. 2015b[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Trials of acoustic harassment have yet not been conclusive in the literature [START_REF] Götz | Target-specific acoustic predator deterrence in the marine environment: Targetspecific acoustic deterrence[END_REF], Tixier et al. 2015b[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Another approach to repeal odontocetes from the fishing gears using acoustic is to attract them toward a decoy producing fishing vessels playbacks [START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF][START_REF] Wild | Field trials of an acoustic decoy to attract sperm whales away from commercial longline fishing vessels in western Gulf of Alaska[END_REF].

These playbacks require to assess the vessels acoustic signatures and determine any acoustic clues which may attract odontocetes [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. Furthermore, the identification of the sounds that attract the odontocetes could also lead to improvement of fishing vessel to decrease their detection range [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. When it seems inefficient to avoid the attraction of odontocetes of the fishing gear, then the last option seems to be the use of physical protections. Although a physical protection is the most complicated method to implement, since it requires changing the fishing gear and so the setting system of the fishing boat, it may offer the most efficient solution. Thus, this approach may be considerably expensive for fisheries but the benefits gained from suppressing depredation may cover the costs engaged. This approach was first developed by Chilean fishermen to mitigate sperm whale depredation from demersal longlines, using a floating net sleeves sliding down to protect the fish during hauling, called the 'Cachalotera' [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Moreno | Significant conservation benefits obtained from the use of a new fishing gear in the Chilean Patagonian toothfish fishery[END_REF][START_REF] Arangio | Minimising whale depredation on longline fishing[END_REF]. In contrast, pelagic longlines may be exposed to depredation during the entire fishing process, i.e. both during soaking and hauling, thus using a triggered device that could encapsulate the fish caught may avoid depredation [START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. This approach has nevertheless not received a lot of attention yet [START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. This may be partially explained by the complexity to test experimental devices on commercial fishing boats not adapted to scientific trials, and to logistical constraint to equip all fishing gear with numerous hooks deployed by commercial longliners. Fishing gear modification can require a period to optimise the new practice, whereas other fishing methods might be already optimised and useful to reduce depredation, such as the fishing pots. For instance in Alaska pot fisheries have been approved and seem to be effective in preventing sperm whale depredation, but are more expensive to use compared to conventional longlines (Sullivan 2015, Peterson and[START_REF] Peterson | Sablefish mortality associated with whale depredation in Alaska[END_REF]. The same conclusion has been drawn after a preliminary trial performed as part of the ORCASAV program in 2010 around Crozet Archipelago (captains and Guinet personal communications, and see [START_REF] Bavouzet | Rapport de la campagne casier Orcasav : Une campagne innovante de pêche aux casiers dans la ZEE de Crozet pour lutter contre la déprédation des orques et la mortalité aviaire[END_REF][START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF]. Furthermore, with the recent expansion of longline fisheries some depredation cases are relatively new and still require some assessments.

As all conservation conflict, depredation requires a good understanding of the issue and then a good monitoring of solutions. Although mitigation is on its way many studies are still at the understanding stage and scientist start to monitor solutions. The complexity of the issue comes from the ecological, socioeconomic and cultural dimension of the conflict. Indeed this interaction involves in one side one of the most important human activity (i.e. fishery); and in the other side some emblematic species which are still poorly known as they evolve in an ecosystem hardly accessible. The knowledge of these mysterious animals has yet substantially increased with the improvement of technologies, such as in acoustics, bio-logging, or molecular biology. With these progresses researcher have recently described unobserved populations or even new species of marine mammals [START_REF] Robineau | Description of a new subspecies of Commerson's dolphin, Cephalorhynchus commersonii (Lacépède, 1804), inhabiting the coastal waters of the Kerguelen Islands[END_REF][START_REF] Pitman | Observations of a distinctive morphotype of killer whale (Orcinus orca), type D, from subantarctic waters[END_REF][START_REF] Morin | Genetic structure of the beaked whale genus Berardius in the North Pacific, with genetic evidence for a new species[END_REF] or assessed new insights of diving behaviour by some odontocetes [START_REF] Schorr | First Long-Term Behavioral Records from Cuvier's Beaked Whales (Ziphius cavirostris) Reveal Record-Breaking Dives[END_REF][START_REF] Reisinger | Movement and diving of killer whales (Orcinus orca) at a Southern Ocean archipelago[END_REF]. These studies revealed how much we still have to learn about marine mammals and especially about their underwater behaviours. As previously discussed, the understanding of marine mammal behaviours is crucial to assess the conservation conflicts. This thesis falls in this purpose to better understand interactions between marine mammals and longline fisheries for a better lead to mitigation solutions.

CONTEXT AND PURPOSE OF THE THESIS

CONTEXT OF THE THESIS: A GAP IN FINE SCALE

UNDERSTANDING OF LONGLINES DEPREDATION

Research effort on interaction between odontocetes and longlines has substantially increased over the last 15 years [START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF], Straley et al. 2015, Guinet et al. 2015, Thode et al. 2015, Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. These studies have considerably improved our understanding of these conflicts by identifying the species involved and estimating impacts on fisheries at the fleet level (e.g. [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Dyb | Fisheries depredation experience of the Norwegian longline fleet[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Peterson | Killer Whale Depredation and Associated Costs to Alaskan Sablefish, Pacific Halibut and Greenland Turbot Longliners[END_REF], Straley et al. 2015[START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF] and on wildlife populations, both depredating species and fish stocks (e.g. [START_REF] Gilman | Shark interactions in pelagic longline fisheries[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Forney | What's the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries: Patterns of cetacean bycatch and depredation in Hawaii[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF], Gasco et al. 2015, Guinet et al. 2015, Tixier et al. 2017). However, most of these studies have relied on large spatio-temporal scales and have been limited to visual surface data, such as direct surface observations of depredating animals around fishing boats (especially for demersal longlines, e.g. [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Purves | Killer whale (Orcinus orca) and sperm whale (Physeter macrocephalus) interactions with longline vessels in the Patagonian toothfish fishery at South Georgia, South Atlantic[END_REF][START_REF] Kock | Interactions between Cetacean and Fisheries in the Southern Ocean[END_REF][START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF], Gasco et al. 2015[START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF] or through the presence of damaged fish on hauled longlines (especially for pelagic longlines, e.g. [START_REF] Rosa | Killer whale (Orcinus orca) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF], Straley et al. 2015[START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF]. The underwater dimension of depredation has remained poorly investigated. First studies using alternative types of data, such as underwater videos, acoustics and bio-logging revealed that odontocetes may depredate baits and remove the whole fish [START_REF] Mathias | Relationship between sperm whale (Physeter macrocephalus) click structure and size derived from videocamera images of a depredating whale (sperm whale prey acquisition)[END_REF], Thode et al. 2014[START_REF] Sullivan | North Pacific Fishery Management Council[END_REF][START_REF] Colto | TAC UNDER MSC FULL ASSESSMNET[END_REF], Guinet et al. 2015). These investigations question the accuracies of interaction estimations, and suggest that depredation rates may be underestimated, which could have critical consequences on fishing management and stocks assessments. It is therefore essential to increase research effort on this underwater dimension, and to question our vision of interactions between fishermen and marine predators.

Depredation conflicts have been mainly described as "fishery-odontocetes" interactions, therefore considering the fishing gear and fishermen as a unique entity. Indeed, for demersal longline fisheries, "interactions" have been assumed to primarily occur during hauling phases when animals are seen in the vicinity of fishing boats [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. However, during soaking, the fishing gear is independent from fishermen activity and studies on pelagic longlines revealed that depredation could occur during this phase (Dalla [START_REF] Rosa | Killer whale (Orcinus orca) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions[END_REF][START_REF] Forney | What's the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries: Patterns of cetacean bycatch and depredation in Hawaii[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF][START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF]. Thus, a finer approach may be to assess "fishing gear-odontocetes" interactions separately from the "fishermen-odontocetes" interactions. By dissociating fishermen from their fishing gear, we may consider three "actors" involved in the depredation conflict (fishermen, fishing gears and odontocetes). This categorisation of entities adds the possibility to describe the relationships/interactions between them at different scales. We can therefore assess the "fishing gear-fishermen" interaction which relates to fishermen behaviour in regards to their setting and hauling strategies. Fishing behaviours and strategies include the way fishers use the fishing gear (i.e. longlines' length, the hauling speed, etc.) or the fishing vessels (i.e. manoeuvres, mobility) and make decisions during fishing operations to exploit the fish stock.

Fishermen behaviours and strategies have been poorly assessed at individual scale. As an analogy to other predators, it is of interest to better understand fishermen foraging behaviours within a context of competition with odontocetes [START_REF] Mccay | Systems ecology, people ecology, and the anthropology of fishing communities[END_REF][START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF][START_REF] Orians | Human Behavioural Ecology[END_REF][START_REF] Nettle | Human behavioral ecology: current research and future prospects[END_REF]. Individual foraging behaviours may indeed imply different risks of detectability by the odontocetes. This behavioural ecology approach may highlight mechanisms of the conflict at a "fishermen-odontocetes" dimension. Finally, we may wonder how odontocetes depredate on the fishing gear once the competition starts. Similarly to fishermen behaviour, an individual scale of marine predators behaviour would then tackled gaps in the knowledge of "fishing gear-odontocetes" interactions. This approach requires monitoring odontocetes' behaviours underwater. This PhD aimed therefore at assessing a depredation conflict case from the three interactions dimensions described here before and using individuals' behaviours of both fishermen and odontocetes.

STUDY CASE

This thesis focused on an example of the depredation conflict involving the commercial Patagonian toothfish (Dissostichus eleginoides) longline fisheries with killer whales and sperm whales in the Southern Ocean.

The Patagonian toothfish belongs to the Nototheniidae family and the Dissostichus spp. genus, which includes two species that are both endemic to the southern hemisphere and bathypelagic. While the other species, the Antarctic toothfish, Dissostichus mawsoni, is found at high latitudes around Antarctica, the Patagonian toothfish is primarily found in sub-Antarctic waters [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF]). These are long-lived (>50 years) and large size (> 200 cm in length and > 200 kg in weight) species [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF]. A high-quality flesh makes the two species economically highly valuable [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Grilly | The price of fish: A global trade analysis of Patagonian (Dissostichus eleginoides) and Antarctic toothfish (Dissostichus mawsoni)☆[END_REF] and toothfish fisheries have developed as a primary economic activity of Antarctic and sub-Antarctic waters (Figure 1-2, [START_REF] Croxall | Management of Southern Ocean fisheries: global forces and future sustainability[END_REF][START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Grilly | The price of fish: A global trade analysis of Patagonian (Dissostichus eleginoides) and Antarctic toothfish (Dissostichus mawsoni)☆[END_REF]. The average prices of imports for toothfish have recently increased by 62.24% (between 200762.24% (between -201262.24% (between -Grilly et al. 2015)), with an estimated current value of 30-35 US$/kg. The Antarctic toothfish fisheries started in the 2000s, and are primarily operated in areas regulated by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). The Patagonian toothfish was first caught as a bycatch species by the trawl fisheries operating in the Exclusive Economic Zones (EEZ) of Chile in the 1950s and of Kerguelen and South Georgia in the early 1980s. The species later became a target species between the mid-1980s and the 2000s, initially caught by trawl fisheries until the emergence of demersal longlining, which progressively replaced trawling in the 1990s [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF]. Patagonian toothfish fisheries now primarily operate in areas under CCAMLR jurisdiction, which includes international waters and EEZs around French, Australian, British and South African sub-Antarctic islands, as well as in non-CCAMLR areas such as the EEZs of Argentina, Chile and the Falklands/Malvinas [START_REF] Croxall | Management of Southern Ocean fisheries: global forces and future sustainability[END_REF][START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Grilly | The price of fish: A global trade analysis of Patagonian (Dissostichus eleginoides) and Antarctic toothfish (Dissostichus mawsoni)☆[END_REF]. However, in most of these fishing areas, fisheries have experienced interactions with killer whales and/or sperm whales since the very beginning of their commencement [START_REF] Nolan | Interactions between killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus) with a longline fishing vessel[END_REF][START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Kock | Interactions between Cetacean and Fisheries in the Southern Ocean[END_REF][START_REF] Yates | ) and killer whales (Orcinus orca) in the Falkland Islands longline fishery:8[END_REF][START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Roch | Classification of echolocation clicks from odontocetes in the Southern California Bight[END_REF][START_REF] Arangio | Minimising whale depredation on longline fishing[END_REF], Guinet et al. 2015). In some fisheries the probability to haul a longline in presence of sperm whales have exceeded 50%, such as in Chile or around the French sub-Antarctic islands [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. These interaction levels led to significant decrease to fishing success, with killer whales being the species causing greater fish losses than sperm whales [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF]. Therefore, these interactions with killer whales and sperm whales may have serious implications for the economic viability of the toothfish fisheries as well as for sustainable management of fish stocks, raising the necessity to find mitigation solutions.

The depredation conflict existing in the fishery operating in the French EEZs of Crozet and Kerguelen islands is a study case with suitable characteristics to address the aims of the thesis. Firstly, this fishery holds the largest Patagonian toothfish quota in the Southern Ocean [START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF][START_REF] Colto | TAC UNDER MSC FULL ASSESSMNET[END_REF] and experiences the highest depredation levels of all other toothfish fisheries, with more than 30% and 9% of the total annual catch taken by killer and sperm whales, respectively at Crozet and Kerguelen [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF], Gasco et al. 2015[START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. Secondly, this fishery is composed of 7 legal longliners ranging from 50 to 60 m long with crews composed of 30 fishermen and has been fully controlled and monitored since the early 2000s, with fishery observers monitoring 100 % of the fishing operations. The same fleet operates in both the Crozet and Kerguelen EEZs during a same fishing season, which greatly differ in catch limits (around 80% of the total annual catch is fished in Kerguelen) and in size (the Kerguelen fishing area is 5-7 times larger than the Crozet area). A fishing season runs from September to August, but the fleet is not allowed to fish in the Kerguelen EEZ for a 45-day period from the 1st February to mid-March to comply with seabird conservation measures. A season is composed of 3 to 4 trips lasting between 2.5 and 3 months. During a trip, vessels fish continuously through a diel pattern. Longlines are set at night and mostly hauled during the day, since fishing regulations prohibit setting at daylight to avoid seabird bycatch [START_REF] Weimerskirch | Factors affecting the number and mortality of seabirds attending trawlers and long-liners in the Kerguelen area[END_REF]. These vessels use auto-weighted longlines set between two anchors and linked to buoys at the surface for retrieval. The lines are composed of sections of 750 hooks spaced every 1.2 m (Figure 1-1). The length of these lines can vary from 1 to 40 km, averaging 8 km. The hooks are automatically baited and set on the seafloor at depths ranging from 500 to 2500 m. Fishing in waters shallower than 500 m is prohibited to avoid the capture of juvenile toothfish [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Gasco | Contributions to marine science by fishery observers in the French EEZ of Kerguelen[END_REF]. Since 2003 the two EEZs are also subject to a high level of surveillance by the French government, which has resulted in negligible levels IUU fishing, further enhancing the representativeness of the data collected from licensed vessels [START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF]. Besides, interaction levels are sensitively different between the two EEZs since sperm whales are present around both islands but at different densities [START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF], whereas killer whales are almost exclusively found in Crozet [START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF], Guinet et al. 2015). Furthermore, the Crozet Archipelago is one of the few locations in the Southern hemisphere where killer whales have long been studied, allowing a monitoring of the population before and after the fishing activity started (Guinet et al. 2015). This unique study case gives the opportunity to understand the impact of the fishing activity on an odontocete population. Indeed, in Crozet the fishery started in 1996 with a lot of IUU fishing vessels which negatively impacted the killer whale population [START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF], Tixier et al. 2017). The objectives of OrcaDepred are to better understand the depredation behaviour and ecology of cetacean species involved to offer fishing companies operational and technological solutions to depredation. Thus, the project aims at:

1.

Better understanding the natural feeding and interaction behaviours with the fishery cetaceans interacting with the lines, and in the case of pelagic longlines identify the cetacean species involves.

2.

Assessing the bio-economic consequences of depredation through an ecological economic modelling exercise for the sustainable management of these fisheries taking into-account depredation.

3.

Assessing the ecological consequences of fishing-depredation at the ecosystem level.

4.

Assessing if the levels of interaction between cetaceans and ships are related to fishing practice differences between captains and/or vessel characteristics, with a special focus on acoustic noise generated.

5.

Implementing a technological approach to remove depredation. In partnership with industry, new prototypes of fish protection devices on the lines and not harmful to cetaceans. Islands) and on the blue-eye trevalla demersal longline fishery in South East Australia.

THESIS OBJECTIVES

My PhD thesis contributes to the OrcaDepred project, and to the French-Australian collaboration on longlines' depredation. Indeed, my thesis is set as a cotutelle between the University of La Rochelle (Chizé, France) and Deakin University (Melbourne, Australia).

My work mostly focused on the 1 st and 4 th purposes previously described (section 1.3.2), using the French Patagonian toothfish fishery as a study case. More specifically, the aim of my thesis was to bring new insights on the interaction between odontocetes and demersal longlines using both human and animal behaviours at individual scales. My first objective was to examine the drivers of observed variations of interaction rates between captains. This problematic was assessed through a human behavioural ecology approach (chapter 2) and an acoustic approach (chapter 3). My second objective was to assess how, when and where depredation occurs on demersal longlines. A passive acoustic monitoring and a biologging approach were suggested to bring some answers to this question (chapter 3 and chapter 4). Thus the thesis was organised around the three methodological approaches based upon research articles and technical protocol:

-Chapter 2: The first approach aimed at better understanding the behaviour of captains facing the depredation and which factors drive their foraging strategies, using operational data collected on fishing boats and methods of behavioural ecology.

-Chapter 3: This chapter focused on passive acoustic monitoring. I assessed what this method could bring on the understanding of depredation in the underwater dimension and I specifically focused on the underwater detectability of the fishermen to bring insights for the first problematic.

-Chapter 4: I finally assessed how bio-logging technologies could bring important information on the interaction between longlines and odontocetes. Bio-loggers were deployed on odontocetes but also on the fishing gear to bring an original double perception of the interaction.

Altogether, these new insights are expected to bring new perspectives for fisheries assessment and mitigation solutions, which are discussed in Chapter 5.

Pre-existing data were really scarce at the beginning of my thesis. To fulfil my objectives, I aimed at: (i) deploying an acoustic array to assess acoustic tracking and record vessels acoustic (chapter 3); (ii) deploying loggers (accelerometers) on longlines (chapter 4);

(iii) deploying loggers on odontocetes with on-board processing acceleration data. Conducting experiments at sea was thus an important part of my PhD. My contribution covers experimental design, planning, logistics, and at-sea realization. This will be briefly summarized below.

THESIS FIELD EFFORT

No hydrophone and no loggers (both on longline and odontocetes) have ever been deployed from the French and Australian longliners to investigate depredation before my thesis.

The first step of my PhD was therefore to choose adapted instrumentation, set the protocols and prepare the missions for the deployments. I did this in close collaboration with both instrument manufacturers and fishermen. An important point here was to ensure that scientific activities would minimally disturb fishing activities, in order to maintain collaboration with fishermen.

As part of an experimental design, I have tested an acoustic tracking methods during my master thesis (prior to this PhD) to determine the best setting for an acoustic array (see Appendix 1).

The setting was imparted to a French acoustic company (RTSYS) which designed the array.

Another experimental design, assessed at the beginning of this PhD, was to test whether a method derived from acceleration analyses on southern elephant seals could be used on sperm whales (section 4.2). The results and method adapted to a sperm whale dataset was then transmitted to Wildlife Computer, a biologging company. The company embedded this algorithm on new generation of loggers to be deployed on marine predators, e.g. for odontocetes in our study but also new tags for fish and sharks. As for the loggers deployed on longlines, they have been directly developed by Dominique Filippi (Sextant Technology, New Zealand).

In a second step, I personally conducted the sea-trials from the French fishing fleet. I have therefore conducted two missions of 3 months each during the fishing seasons of 2016-2017 and 2017-2018, aboard three longliners during the first mission and two longliners during the second mission (see Table 1-1). Loggers on longlines have been deployed during the two missions (Table 1-1). The first mission allowed testing and improving the protocol and during the second mission I had more loggers to increase the sampling effort. This experiment works well, but with a higher number of loggers I experienced an increase number of losses and breaks. The loggers on odontocetes have been deployed only during the second mission (Table 1-1) since it took some times to receive the new logger generation. Tagging condition on fishing vessels has been revealed to be very complicated. A successful tagging requires very good condition both from the weather and the approach of individuals. As the shoot occurs on the deck at 5 m above the water (without any swell), individual should be close enough to keep enough power when the dart hit the skin to fix the tag. Besides, the wind should be low to avoid strong deviation of the dart trajectory and allow hitting perpendicularly the individual. Despite these constraints, my experience revealed that it is doable to equip killer whales. Although sperm whales are seen more often, good tagging opportunities are less common than for killer whales.

The acoustic array was deployed during both mission (Table 1-1), but the setting evolved between the two missions to deal with several issues. The first mission allowed noticing that the acoustic array composed of 4 hydrophones connected through cables to one recorder was not adapted to the conditions experienced on a longliner within the southern ocean. The array was therefore changed during the second mission by 4 independent acoustic recorders (Soundtraps, Ocean Instruments, NZ). Using independent hydrophones facilitate the deployments. Additionally, I have set a protocol to deploy a single independent hydrophone by fishermen and fishery controllers on all the longliners for every trip (7 French vessels and 4

Australian vessels). Besides, the protocol is also used on both French and Australian blue-eye trevalla longline fisheries to monitor interactions with killer whales. The purpose of this protocol is to assess vessel acoustics and allow a passive acoustic monitoring of odontocetes.

The hydrophones stay on-board and are deployed by fishermen under the supervision of the fishery observers who collect the data. The protocol will improve through fishermen and observers feedbacks to allow a future long term acoustic monitoring of the interaction between longliners and odontocetes.

In addition, I have processed photo-identification and biopsy sampling which were not analysed in this thesis but important for the OrcaDepred objectives. Besides, during the second mission I have also tested individual protection systems for hooks (Chapter 5), designed by an industrial provider (SATIM). The tests were not conclusive but were important to focus on better countermeasures for the future. This experience has indeed allow the SATIM company to work on a new generation of protection system easier to implement considering all the constraint from the setting and hauling processes. I have finally experienced the role of fishery observer during a month, observation which fuel the PECHKER dataset [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF] managed by the Museum d'Histoire Naturelle de Paris. This dataset was graciously shared by the MNHN, allowing the study on captains' behaviour (section 2.2). Through the different steps of the missions' management I have been able to interact with the different protagonist of the fishery: fishermen, fishing companies, fishery observers, units of the TAAF and MNHN allowing a better understanding of the depredation issue from the different points of views.

FISHERMEN BEHAVIOURAL ECOLOGY

Most of previous studies have relied on large spatio-temporal scales and at the fleet scale to improve our understanding of these conflicts assessing the impact of depredation on fisheries and within fisheries how some practices, i.e. operational factors, could influence depredation rates. However, these studies did not really dissociate the fishing gear from the fishermen within fisheries, whereas fishermen (especially captains) are the decision makers playing on the operational factors. This chapter aims therefore at reviewing within the French Patagonian toothfish fisheries how an operational approach might help toward some mitigation measures.

Additionally, the purpose of this chapter is to focus on the decision makings of captains and to suggest a finer scale could ensue from this operational approach through a human behavioural ecology.

INTRODUCTION

WHAT IS AN OPERATIONAL APPROACH?

Operational factors include all variables controlled by captains, and that can therefore be adjusted to act on either the probability of interaction with odontocetes or the extent of depredated fish during interactions. Mitigating depredation through operational approaches involves adapting the fishing practice in a way that it reduces depredation while maintaining high fishing success. Developing operational mitigation strategies therefore involves determining the causal connections between operational factors, depredation and fishing success. Such research is often challenging to implement since it requires an extensive monitoring of the fishing practices and of the occurrence of depredation. The French Patagonian toothfish fishery (cf. section 1.3.3.) is subject to a long-term full monitoring of fishing operations, allowing such approach.

Aboard the French longliners, fishing operations are monitored by fishery observers mandated by the administration of the French Antarctic and Southern Territories (TAAF), tasked for scientific monitoring by the National Museum of Natural History (MNHN) [START_REF] Gasco | Contributions to marine science by fishery observers in the French EEZ of Kerguelen[END_REF]). The MNHN is the entity in charge of conducting the fish stock assessments and estimating the Total Allowable Catch (TAC) used to define quotas. The data collected by fishery observers are stored within the so-called PECHEKER database [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF], which includes data on the species targeted, the quantity of fish caught and fishing locations (setting depths and geographic positions, see [START_REF] Gasco | Contributions to marine science by fishery observers in the French EEZ of Kerguelen[END_REF]. Observers also sample fish length and weight for resources assessments and record all by-catch of non-target species (e.g. grenadiers species, antimora, or skates species), as well as interactions with protected seabirds and marine mammals [START_REF] Gasco | Contributions to marine science by fishery observers in the French EEZ of Kerguelen[END_REF]. Thus, the PECHEKER dataset provides information on operational variables such as the time, location, setting depth, number of hooks, as well as information on the total weight of toothfish caught, the occurrence of depredation and the number of depredating sperm whales and killer whales for every longline set. Visual observation of odontocetes around the vessels during hauling defines an interaction. These observations enable later estimation of interaction rates at different temporal scale (daily, monthly, per trip, per fishing season, etc) or at different spatial scale (longlines, sectors EEZ).

The first step of the operational approach is to assess whether some fishing practices (e.g. location, setting depth, length of longlines, fishing season…) may influence the probability of odontocetes to interact with longlines. The second step is to assess, when interaction is unavoidable, whether some fishing practice may reduce the impact of depredation on the fishing success. The catch per unit effort (CPUE, in g.hooks -1 ) is a relevant proxy of the fishing success.

The fishing effort is considered as the number of hooks (H) set, and the catch is the sum of the biomass (M in g) of Patagonian toothfish hauled for the given number of hooks. The CPUE for a given unit i (longline, sector, trip, etc.) is estimated as:

EQUATION 2-1: 𝑪𝑷𝑼𝑬 (i) = ∑ 𝑴 (𝒊) ∑ 𝑯(𝒊)
The CPUE is also used to estimate depredation rate, i.e. the loss of biomass due to odontocetes.

The depredation rate is then assessed as the difference of CPUE on longlines hauled in absence of odontocete (set as the reference or the resource availability) and the CPUE on longlines hauled in presence of odontocetes [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF]. Depredation rate can also be estimated by the change in proportion of by-catch species such, as grenadier species, which are not depredated by killer whales and sperm whales, in presence or absence of toothfish depredation [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF].

RECOMMENDATIONS ON FISHING PRACTICE TO MITIGATE

DEPREDATION

From the PECHEKER database [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF], probabilities of interactions and depredation rates for sperm whales and killer whales have been assessed in previous studies at different levels: longline set, fishing vessel, fishing fleet. These studies aimed at identifying correlations between operational variables and interaction/depredation rates [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF], Gasco et al. 2015). From results obtained at the longline level, advice has been given to fishermen on their fishing practice. For instance, fishermen should haul their longlines at a greater speed (hooks.min -1 ) since it reduces depredation rates by making the interaction with the hooks more difficult for both species but increasing hauling speed can also induce a greater loss of lightly hooked fish (Tixier et al. 2015c[START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF] see Appendix 4). Similarly, shorter longlines were shown to have lower depredation rate with killer whales as these longlines are fully hauled within shorter time frames and thus limit the time needed by odontocetes to detect, arrive and interact with the longline (Tixier et al. 2015c), but this practice become inefficient once killer whales of sperm whales have started to interact with a fishing vessel in a given location. Shorter soaking time was also shown to reduce opportunities for sperm whales to arrive to remove large amount of fish from longlines (Janc et al. 2018, see Appendix 4). Another factor influencing interaction and depredation rates for both species is the setting depth (Tixier et al. 2015c[START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]). However, this variable depicts the environment since the depth may reflect some parts of the shelves or seamounts where the odontocetes may forage naturally and are thus overlapping with fishing activity. As the contrary, deeper sets than 1000 m are beyond the foraging range of killer whales [START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF]. At the fleet level, seasonal trends in the probabilities of interaction with odontocetes around Crozet and Kerguelen have been observed for both species (Tixier et al. 2015c[START_REF] Colto | TAC UNDER MSC FULL ASSESSMNET[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. For instance, fishermen were advised to fish in Crozet around November-December where the interaction rate with Crozet killer whales are the lowest [START_REF] Tixier | Depredation of Patagonian toothfish ( Dissostichus eleginoides ) by two sympatrically occurring killer whale ( Orcinus orca ) ecotypes: Insights on the behavior of the rarely observed type D killer whales[END_REF]).

This might be explained by the breeding period of southern elephant seals, Mirounga leonina, from October to December, during which some killer whales units feed upon newly weaned elephant seal pups [START_REF] Guinet | Comportement de chasse des orques (Orcinus orca) autour des îles Crozet[END_REF][START_REF] Guinet | Development of intentional stranding hunting techniques in killer whale (Orcinus orca) calves at Crozet Archipelago[END_REF], Guinet et al. 2015[START_REF] Tixier | Depredation of Patagonian toothfish ( Dissostichus eleginoides ) by two sympatrically occurring killer whale ( Orcinus orca ) ecotypes: Insights on the behavior of the rarely observed type D killer whales[END_REF]. Fishermen were also advised to focus their effort during winter (June-August) when the interactions rates with sperm whales become insignificant within Kerguelen EEZ and two times lower than summer within the Crozet EEZ [START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF], since individuals migrate to warmer waters in low latitudes for reproduction purposes [START_REF] Madsen | Male sperm whale ( Physeter macrocephalus ) acoustics in a high-latitude habitat: implications for echolocation and communication[END_REF][START_REF] Mellinger | Seasonal occurrence of sperm whale (Physeter macrocephalus) sounds in the Gulf of Alaska, 1999-2001[END_REF][START_REF] Teloni | Shallow food for deep divers: Dynamic foraging behavior of male sperm whales in a high latitude habitat[END_REF]. However, for fishermen, some of these recommendations may be challenging to implement since other parameters shape their fishing timing. For instance, during winter the conditions are rough in these high latitudes, making fishing not always possible for safety reason. Variations of odontocetes occurrence may not only be temporal but also spatial. However, first insights on spatial distribution of interactions revealed that no fishing spots used by fishermen were free of odontocete depredation [START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Colto | TAC UNDER MSC FULL ASSESSMNET[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Labadie | Sperm whales of the Crozet and Kerguelen islands -Photo-identification catalogue[END_REF], 2018). Besides, as fishermen are in competition with odontocetes, it is more likely that predators already forage naturally within areas with high toothfish densities. First spatial analyses emphasize the necessity to better understand the natural ecology and behaviour of these species as it may help to predict their occurrence on fishing areas. At a finer spatial scale, the effect of the distances travelled by vessels after hauling a longline on the probability to experience interaction on the longline next hauled was also examined to determine the efficacy of "move-on" strategies (Tixier et al. 2015c[START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. From these studies, fishermen were advised to cover at least 60 km in presence of sperm whale [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]) and 100 km in presence of killer whales (Tixier et al. 2015c) to avoid new interaction with these species.

Altogether, these results have brought useful insights on the relationship between some fishing practices and interaction/depredation rates of odontocetes. Identifying better fishing practices may work as an easy-to-implement mitigation measures. However, these recommendations may involve changing several fishing practices inconsistent within a fishing strategy. For instance, suggesting to set shorter longlines at deeper depths may be unproductive for fishermen as it would result in spending more time to haul the ropes between the buoys an the anchors than hauling the longline. Thus, further investigations should be conducted at a finer scale exploring optimal fishing strategies made of a combination of fishing practices.

BEHAVIOURAL ECOLOGY OF CAPTAINS

The monitoring of an optimal combination of fishing practices requires assessing the strategies of the decision-makers: the captains. As the operational approach consists to assess whether some fishing practices may influence the probability of interaction with predators, it is therefore essential to assess in a first step whether captains reveal differences of interaction The daily interaction was estimated as the ratio of longlines hauled in presence of odontocetes and the total number of longlines hauled per day. I then represented the variations mean daily interaction rates per trip for each captain on a given vessel through boxplots [START_REF] Mcgill | Variations of Box Plots[END_REF]. The boxplots depicted the median with the 25 th and 75 th percentiles (the two hinges). Also a 95% of confidence interval through was represented (the two whiskers) with the outlying points shown individually [START_REF] Mcgill | Variations of Box Plots[END_REF].

Captains who performed at least one fishing season (i.e. 3 different trips) with a same vessel were kept for the graphic representation to allow median and percentiles estimations. Boxplots

were assessed with the function geom_boxplot (package ggplot2) through the software R (R Development Core Team 2015). Firstly, this exploratory analysis showed on Figure 2-1 that in some cases, different captains working on the same vessel had a similar interaction rate but different to other vessels, e.g. between vessels 5 and 8 with sperm whales at Crozet. These differences of interaction rates might be related to vessels' specificities. A hypothesis which will be tested in this thesis is that vessels may have different acoustic signatures and thus different detectability by odontocetes (see section 3.3). Secondly, I also observed on Figure 2 Relationships between fishermen and their environment have mostly been assessed

through the causalities between the operational factors and fishing success and/or depredation (section 2. 1.2). Distinction between fisheries, fishermen and fishing gears has indeed not always been carried out. However, fishermen adapt the operational factors to the environmental conditions, such as the abundance of toothfish or the presence of odontocetes, to optimise their cost-benefit ratio of the fishing practices. Their purpose is thus to maximise their fishing success for a lowest associated costs, for instance gas consumption while navigating. The combinations of these practices define strategies and are more likely to vary between fishermen according to their perception of the environment. Thus, further investigations should focus on the decision makers of the fishing strategies: i.e. the captains of the French longliners. I suggested in this thesis to use a behavioural ecology approach to study captains' decisions [START_REF] Nettle | Human behavioral ecology: current research and future prospects[END_REF]) :

i.e. the optimal foraging theory [START_REF] Pyke | Optimal Foraging Theory: A Critical Review[END_REF][START_REF] Real | Risk and Foraging in Stochastic Environments[END_REF][START_REF] Kamil | Foraging behavior[END_REF][START_REF] Charnov | Optimal foraging: some theoretical explorations[END_REF][START_REF] Nettle | Human behavioral ecology: current research and future prospects[END_REF]. According to the optimal foraging marginal value theorem [START_REF] Charnov | Optimal foraging, the marginal value theorem[END_REF]) as foragers, captains' decisions should maximise their "energetic" return. As the strategy of the captains is not to feed themselves but rather to fish for their company, the "energy" is in fact "money" in the broad sense (economical value with the fish and cost of fishing operations). The captains' decisions should then maximise the money return, which is perceived on the fishing boat as an optimisation of the fishing success. During the different stages of the fishing process, captains have choices to make: i) while prospecting for the fish resource (i.e. setting) and ii) when exploiting the resource once found (i.e. hauling). The aim of this chapter was therefore to understand captains' decision-makings at these i) inter-patch (section 2.2) and ii) intra-patch (section 2.3.1) scales within a context of competition with odontocetes. The hypothesis is that they should optimise their prospection and maximise the exploitation of the resource and aim at minimising the competition. An assessment of how depredation impacts captains' behaviours may enable using an optimal strategy as a mitigation solution. This human behavioural approach aimed to modelise captains' decisions with ecological theories independently to captains' feedbacks. In a further stage, captains can provide ground truth to confirm models.

ARTICLE: DO COMMERCIAL FISHERIES DISPLAY OPTIMAL FORAGING? THE CASE OF LONGLINE FISHERS IN COMPETITION WITH ODONTOCETES

ABSTRACT

Depredation in longline fisheries by odontocete whales is a worldwide growing issue, having substantial socio-economic consequences for fishers as well as conservation implications for both fish resources and the depredating odontocete populations. An example of this is the demersal longline fishery operating around the Crozet Archipelago and Kerguelen

Island, southern Indian Ocean, where killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus) depredate hooked Patagonian toothfish (Dissostichus eleginoides). It is of great interest to better understand relationships of this modern fishery with its environment. Thus, we examined the factors influencing the decision making process of fishers facing such competition while operating on a patch. Using optimal foraging theory as the underlying hypothesis, we determined that the probability captains left an area decreases with increasing fishing success whereas, in presence of competition from odontocete whales, it increases. Our study provides strong support that fishers behave as optimal foragers in this specific fishery. Considering that captains are optimal foragers and thus aim at maximising the exploitation of the resources, we highlight possible risks for the long-term sustainability of the local ecosystems.

Keywords: cetacean depredation, longline fishery, Patagonian toothfish, optimal foraging theory, fishing strategies

INTRODUCTION

The way humans acquire marine living resources from the environment has become, over the past 60 years, a major field of research [START_REF] Flaaten | The invisible resource rent in limited entry and quota managed fisheries: the case of Norwegian purse seine fisheries[END_REF][START_REF] Coleman | Overexploiting marine ecosystem engineers: potential consequences for biodiversity[END_REF][START_REF] Wezel | Agroecology as a science, a movement and a practice. A review[END_REF]. While previous efforts have largely been oriented towards improving technology, the processes of decision making and strategy choices have recently received growing attention [START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF][START_REF] Holt-Giménez | Agroecology, Food Sovereignty and the New Green Revolution[END_REF], Straley et al. 2015, Tixier et al. 2015c). In commercial fisheries, the increasing difficulty in predicting the variation of a resource abundance and distribution, mostly due to climate change (Simpson et al. 2011, Pinnegar et al. 2016), paired with growing economic competition, makes optimizing strategies challenging for fishers. Indeed, the marine environment is unpredictable and resource evaluation and decision making for fishing activity appear to be difficult [START_REF] Acheson | Anthropology of Fishing[END_REF].

To increase our understanding of fish stocks and their ecosystems, as well as to improve fishing strategies, human ecologists have used the "optimal foraging theory" (OFT) to investigate the behaviour of fishers [START_REF] Mccay | Systems ecology, people ecology, and the anthropology of fishing communities[END_REF], 1981[START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF][START_REF] Aswani | Patterns of marine harvest effort in southwestern New Georgia, Solomon Islands: resource management or optimal foraging?[END_REF]). The OFT was derived from economic models and has been broadly implemented in studies of animal foraging behaviour to examine various ecological issues as adaptation, competition and energy flow [START_REF] Pyke | Optimal Foraging Theory: A Critical Review[END_REF][START_REF] Real | Risk and Foraging in Stochastic Environments[END_REF][START_REF] Kamil | Foraging behavior[END_REF][START_REF] Charnov | Optimal foraging: some theoretical explorations[END_REF]. Within the OFT framework, energy flow is a key component of ecological systems. Fused with natural selection principles, whereby feeding strategies may evolve by natural selection, foragers choose the behavioural option that maximizes energy intake while minimizing costs [START_REF] Charnov | Optimal foraging, the marginal value theorem[END_REF]).

Fishers primarily rely on environmental clues, paired with their perception about stock availability, to decide whether to stay or leave a resource patch [START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF]. Consequently, the OFT is useful to predict the movement and decisions of fishers, helping to understand their environment and, more precisely, the resource availability [START_REF] Aswani | Patterns of marine harvest effort in southwestern New Georgia, Solomon Islands: resource management or optimal foraging?[END_REF]. Previous studies focusing on traditional fisheries showed that fishers harness their environment as effectively as possible, which results in a short-term goal of energy maximization, i.e. fishing success [START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF][START_REF] Aswani | Patterns of marine harvest effort in southwestern New Georgia, Solomon Islands: resource management or optimal foraging?[END_REF]). However, this occurs with low concern of the marine environment's long-term sustainability [START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF][START_REF] Aswani | Patterns of marine harvest effort in southwestern New Georgia, Solomon Islands: resource management or optimal foraging?[END_REF].

Modern fishers are confronted with increasing competition and interactions with marine wild predators for the same resources, thus influencing fishers' strategic choices [START_REF] Demaster | Predation and competition: the impact of fishereis on marine-mammal populations over the next one hundred years[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. Odontocetes depredation (i.e., toothed whales removing fish from fishing gear) has been described as the greatest impact on decisions made by fishers [START_REF] Sivasubramaniam | Predation of tuna longline catches in the Indian Ocean, by killer-whales and sharks[END_REF][START_REF] Dahlheim | Killer whale (Orcinus orca) depredation on longline catches of sablefish (Anoplopoma fimbria) in Alaskan waters[END_REF][START_REF] Peterson | Whale interactions with Alaskan sablefish and Pacific halibut fisheries: surveying fishermen perception, changing fishing practices and mitigation[END_REF]. In some fisheries, fishers are forced to modify their fishing behaviour and to implement strategies of avoidance of odontocetes resulting in substantial losses for the fishing industry [START_REF] Peterson | Whale interactions with Alaskan sablefish and Pacific halibut fisheries: surveying fishermen perception, changing fishing practices and mitigation[END_REF], Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. One example is the Patagonian toothfish (Dissostichus eleginoides) demersal longline fishery operating around the Crozet Archipelago and Kerguelen Island, southern Indian Ocean (Tixier et al. 2015c).

Since 2003, interactions with killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus) have been reported on > 75% of the longline sets that were hauled around Crozet Archipelago [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF], 2015c). The amount of depredated fishes was estimated to be 30% and 9% of the total catch of Patagonian toothfish at Crozet and Kerguelen, respectively [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF]. Such high level of depredation paired with the small size of the fishing fleet (i.e. only 7 licensed 50-metres long longliners) and the long-term full monitoring of the fishery have made the Crozet and Kerguelen situations a unique opportunity to identify solutions to mitigate interactions with odontocetes. Previous studies have resulted in a number of recommendations being made to the fishing industry to avoid/reduce depredation by cetaceans [START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF], Straley et al. 2015, Tixier et al. 2015c). For instance, one of these recommendations was to move away from patches when depredation occurs and to travel more than 100 km (Tixier et al. 2015c).

However, preliminary analyses of data collected around Kerguelen and Crozet showed that depredation may not be the only factor driving decisions made by fishers. For instance, in some cases fishers may decide to stay on a patch although depredation by killer whales is occurring and, conversely, they may leave and travel between patches when depredation is not reported. These travel phases are costly for the operators because fishers do not catch fish but still incur costs (e.g. fuel and wages). In terms of OFT, the vessel has null energy intake (fishing success) but increasing energy expenditure (fuel) when traveling. At the fishers' scale, these non-fishing phases are also costly since the major part of their salary is based upon the quantity of fish caught during a trip. Fishers have indeed a minimum fixed salary for their whole trip, whether they fish or travel, but if they fish more then they will earn more money. As an analogy with foragers, fishers have a null energy intake (no additional wages) when travelling whereas during fishing phase they have a positive energy intake (additional wages due to fishing success). We therefore hypothesised that fishers should aim to reduce the distances and the time spent travelling between patches to follow an optimal foraging strategy. In the present study, the aim was thus to identify the factors influencing the fishers' decision making process while they are operating on a patch. More specifically, we examined the respective role of fishing success (the daily mass of toothfishes hauled) and the depredation by killer whales and sperm whales on the probability that fishers leave or stay on a given patch. Ultimately, the aim was to determine whether their decision to stay or to leave matches with the OFT predictions.

MATERIAL AND METHODS

DATA COLLECTION AND FIELD SITES

From January 2008 to July 2015, 22 captains operating on eight legal longliners (50 m long vessels) hauled 6387 longlines within the Crozet Islands Exclusive Economic Zone (hereinafter EEZ) and 19 480 longlines within the Kerguelen Island EEZ ( Figure 2-2). Fishing occurs all year round, and a fishing season spans from September to August. For a given vessel, a fishing season is composed of three trips of 3 months each during which the fish caught are processed, frozen, and stored on board.

For every longline set, fishery observers collected data for resource assessment (e.g. such as fish mass) and data about interactions with marine predators. Data were available through the PECHEKER database (Museum d'Histoire Naturelle de Paris; [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF]. An interaction was defined as when cetaceans were observed making repeated dives within an approximate 500 m range from the vessel. This was further quantified using three classes: (i) whales absent (condition suitable for a confident observation), (ii) whales present and (iii) uncertain-observation (conditions unsuitable and/or no observation undertaken). Only 9% and 13% of all longlines were assessed as uncertain-observation for killer whales and for sperm whales, respectively. These longlines were kept in the dataset to determine hauling session (see next section) and to estimate fishing success. Since we worked at a scale of several longlines (cf. hauling sessions) these uncertain-observation did not skew the estimation of interaction for our unit of analysis. In addition, when longlines were hauled in presence of whales, observers provided an estimation of the minimum and maximum number of individuals interacting with the boat.

DETERMINATION OF HAULING SESSION

Fishers can set longlines only at night to avoid birds' by-catch issues. Such constraint shapes their fishing activity as during the day, they can only haul the longlines. At nighttimes, captains can decide either to carry on hauling longlines or to set new ones. Using this alternation of fishing operations between hauling and setting, we defined a hauling session as a temporal succession of hauled longlines. In other word, a hauling session starts with the first hauled longline after a setting and it ends with the last hauled longline before setting new ones. We defined a longline hauling session carried out by a captain as the unit of analysis for our statistical models (see below). In addition, we only considered hauling sessions with at least 3 longlines to be able to estimate mean values from the dataset, since in statistics a mean could not be obtained from n < 3.

From the fishery observer dataset, we assessed for each hauling session the daily mass of toothfish caught (kg.day -1 ) and the distance covered at the end of the hauling session (km) before starting setting new longlines. We also recorded whether cetaceans were present during the session, i.e. whether cetaceans interacted at least with one recorded longline, and the mean number of individuals present during the session.

DEFINITION OF THE DECISION TO STAY OR LEAVE A PATCH

Our purpose was to describe the decision of captains to leave or stay in a patch.

Considering that resource could not be clearly spatially defined in the ocean, we determined a 'patch' as defined in the optimal foraging theory (OFT). In other words, a patch is a spatial area where fishers are foraging, since a patch of toothfish cannot be determined. We, thus, defined a 'patch' as the localization where fishers haul longlines during a hauling session. We then determined whether captains "stay" or "leave" their current patch at the end of a hauling session before setting new lines. To do so, we applied a piecewise regression over the distribution of the distances after hauling sessions to identify a threshold [START_REF] Toms | Piecewise regression: a tool for identifying ecological thresholds[END_REF] defining whether captains leave a patch or not. The same method was applied both in Crozet and in Kerguelen, using the package SiZer [START_REF] Sonderegger | Using SiZer to detect thresholds in ecological data[END_REF]) through the software R (R Development Core Team 2015). Thus the threshold determined a variable: 'stay' or 'leave', for both EEZ. We named this variable 'Leave' and we set for this variable either 0 if a captain stays or 1 if a captain leaves after a hauling session.

MODELLING CAPTAINS' DECISION TO STAY: GENERALIZED LINEAR MIXED MODEL (GLMM)

We aimed at assessing the relationship between the decision of captains to leave a patch, at the end of a hauling session, and the fishing success paired with competition encountered during this session. Thus, the unit of analysis of our model was a hauling session. The fishing success was expressed as the daily mass of toothfish caught per hauling session (kg•day -1 ) within a patch. This proxy of the fishing success was chosen as it is the metric used by captains to determine if they have reached the daily economic threshold (between 2 and 3 tons•day -1 ) and to assess the progress on their allocated fishing quota. The competition is a categorical variable, defined by the occurrence of cetaceans. Four categories were considered: presence of killer whales only, presence of sperm whales only, presence of both species simultaneously, or absence of cetacean (set as the reference category). We also assessed the effect of the interaction between both predictive variables: fishing success and competition. Captains were considered as a random effect to investigate variations of decision between them. The variables were identified to translate our hypothesis to a statistical model [START_REF] Johnson | Model selection in ecology and evolution[END_REF].

However, we could not use a simple linear model since the variable Leave follows a Bernoulli distribution, with a probability π to take the value 1 (leave) and the probability 1-π to take the value 0 (stay). As a result, we used a generalized linear mixed model: GLMM [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]. We thus investigated the relationship between the probability to leave for a captain at the end of a hauling session (π) with the fishing success and the competition, using the link function logit, which is the canonical link for a Bernoulli distribution [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]:

EQUATION 2-2 : logit(πij)= α + aj +(β1+bj)Fishing success ij + β2Competition ij + β3Fishing success ij x Competition ij +ε ij
with πij the probability that a captain j leaves a hauling session i, α the intercept, aj the random intercept for captain j, β1,2,3 the coefficients of the predictive variables, bj the random effect for captain j on the slope of fishing success and ε ij the residual for hauling session i and captain j.

Besides, using a top-down approach [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF], we assessed first the most complex model, i.e. with the interaction between the two predictive variables and the random effect (captain j)

set both on the intercept (α) and on the slope (β1) of the continuous variable (fishing success).

Then, we determined whether the random effect should be applied only on the intercept (aj≠0 and bj =0) through the Akaike Information Criterion (AIC) selection [START_REF] Akaike | A new look at the statistical model identification[END_REF][START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF].

Because Competition is a categorical variable, no slope could be applied, so we did not assess a random effect on β2 and β3. Once the random effect was determined, we removed the nonsignificant variables to select the best model, through the AIC selection, in agreement with the top-down approach. We also compared our models to the null model (logit(πij)= α), using the AIC, to be able to interpret the results. The statistical model was implemented through the software R with the function glmer (package lme4).

Based on the assumption that the random effect for each captain j (bj) on the slope of the fishing success (β1) is significant, we assessed the variability of decision for each captain to stay or leave a ground between both EEZs. We aimed at comparing the slopes of each captain's relationship between the probability to leave an area and fishing success (β1+bj) both at Crozet and Kerguelen. Thus, we assessed a linear regression between these slopes at Crozet and at Kerguelen. To get sufficient number of sessions for each captain on both locations, we considered only captains who spent more than 2 seasons both at Crozet and Kerguelen, resulting in 14 captains. This may enlighten some between-captain variability of decision, according to the fishing area (EEZ).

In addition, we assessed a similar model using the mean number (Nb) of cetaceans present per longline within hauling sessions for killer whales alone, sperm whales alone, or both species simultaneously, instead of the categorical variable 'Competition':

EQUATION 2-3 : logit(πij)= α + aj +(β1+bj)Fishing success ij + (β2+cj)Nb individuals ij + (β3+dj)Fishing success ij x Nb individuals ij +ε ij
with cj and dj the random effects for captain j on the slope of the number of individuals and on the slope of the interaction between the two predictive variables. All continuous variables were standardized. We used again a top-down approach to select the best model, using the AIC.

DESCRIPTION OF A FISHING SUCCESS' THRESHOLD FOR CAPTAINS'

DECISION

Finally we monitored the distributions of the fishing success (kg•day -1 ) among hauling sessions for both cases when captains decide to "stay" and "leave" after the session. The purpose was to compare between the two decisions' cases: how the number of sessions evolve with the increase of the daily mass of toothfish caught. We could then determine for which values of fishing success we have more sessions followed by the decision to stay or by the decision to leave. We thus fitted smoothing splines to both distributions of fishing success (sessions followed by a decision to stay and by the decision to leave), using the function smooth.spline (package stats) through the software R, with a smoothing coefficient of 0.5 to keep at least 50% of the variation. As a result, the intersection between both smoothed distributions approximates the threshold mass of toothfish caught per day at which captains change their decision to stay or leave. Indeed, below the threshold captains may decide to leave their patch. Conversely, above this threshold they may stay on the current patch.

Besides, within the assumption that we would obtain significant interactions between the predictive variables: fishing success and the presence of cetaceans, either as a categorical variable (Competition) or as a continuous variable (Nb individuals), we could determine a threshold of fishing success above which the cetaceans' influence might become ineffective.

To define this threshold, we resolve the equation determining a constant probability to leave irrespective of the competition/number of individuals:

DEFINITION OF THE DECISION TO STAY OR LEAVE A PATCH

We defined through the breaks on the piecewise regression that a captain covering less than 36 km in Crozet and less than 35 km in Kerguelen after a hauling session stayed on the same patch (Figure 234). According to this threshold, 737 and 3,430 hauling sessions, respectively at Crozet and Kerguelen, were followed by a decision to stay. Conversely, 504 and 872 hauling sessions (respectively at Crozet and Kerguelen) were followed by a decision to leave (Figure 2-3 & Supp. data 1). 2-1). This correlation was independent from cetacean depredation as the interaction term was not significant (Table 2-1).

In absence of cetaceans, high between-captains variability was detected on the probability to leave in response to the variation of the fishing success ( Figure 2-5 &Table 2-1 [START_REF] Morrissey | Passive acoustic detection a nd localization of sperm whales (Physeter macrocephalus) in the tongue of the ocean[END_REF][START_REF] Gervaise | Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio[END_REF][START_REF] Gillespie | Automatic detection and classification of odontocete whistles[END_REF][START_REF] Whitehead | Click rates from sperm whales[END_REF][6]. This indicates that captains with the greater negative slope of their probability to leave with a given increase of fishing success at Crozet were the same at Kerguelen (Figure 23456).

Nevertheless, for a given fishing success, all captains showed a higher probability to leave in presence of cetaceans than in absence of interaction, regardless of the species encountered (Figure 2-5 & Table 2-1). For a given foraging success, the probability that captains leave a patch in the Crozet EEZ was 1.6 times higher in presence of both cetacean species than in absence of cetacean (z=3.16, p=0.002, 2-1). At Crozet, the probability of captains to leave when killer whales were the only depredating species was 1.2 times higher than in absence of cetacean (Figure 2-5.a.).

Although this difference was not significant (z=1.03, p=0.3, Table 2-1). According to the AIC selection, the best model explaining the probability for captains to leave in relation to the daily mass of toothfish caught and the mean number of cetaceans (regardless of the species at Crozet) included the interaction between the two predictive variables as well as the random effect on both the slope and the intercept (Supp. data 3):

EQUATION 2-6 : logit(πij)= α + aj +(β1+bj)Fishing success ij + β2Nb cetaceans ij + β3Fishing success ij x Nb cetaceans ij +ε ij
For the model assessing the number of killer whales (at Crozet), the best model was the simplest model (i.e. no interaction and the random effect on the intercept, see Supp. data 3):

EQUATION 2-7: logit(πij)= α + aj +β1Fishing success ij + β2Nb killer whalesij +ε ij
Finally, the best model assessing the probability to leave with the fishing success and the number of sperm whales alone excluded interaction between predictive variables, and it included a random effect over the slope of the number of individuals at Crozet (Supp. data 3): The fishing success had a significant negative effect on the probability of captains to leave, for all the models (Table 2-2). In addition, for a given fishing success, the probability to leave was significantly and positively influenced by the number of cetaceans, regardless of the species (Figure 2-7.a. & Table 2-2). In addition, high between-captain variability in the probability to leave an area in response to the variation in fishing success was detected (Figure 2-7.a. & Table 2-2). The same positive and significant relationship was obtained between the probability of captains to leave and the number of sperm whales alone, both at Crozet (Figure Conversely, at Crozet, the between-captain variability (i.e. random effect) in the probability to leave was observed on the response to the number of sperm whales (Figure 2-8.a. & Table 2-2).

For the sessions with only killer whales, the correlation between the probability of captains to leave and the number of killer whales was positive but not significant (z=1.75,

p=0.08, Figure 2-9. & Table 2-2).
In addition, when considering any of the two cetacean species, the interaction term between the two explanatory variables (daily mass of toothfish caught and number of cetaceans)

was negative and significant (z= -2.39, p=0.02, Table 2- Besides, this relationship changed from positive to negative for a given toothfish daily mass (Figure 2-7.c., see following section for the threshold determination). As a result, we observed that the probability to leave a patch decreased when the number of cetaceans increased for masses of toothfish caught per day above 5866 kg.day -1 (Figure 2-7.c).

DISCUSSION

ARE FISHERS OPTIMAL FORAGERS?

Using OFT, we were able to describe the decision process of longline fishing vessel captains. Our results provided strong support for the assumption that captains act as optimal foragers during fishing operations. We highlighted that decisions made by captains to stay or leave a patch were primarily driven by their fishing success, which is analogous to foraging success in OFT, but was also influenced by cetacean depredation.

Influence of fishing success

Our study showed that fishing success clearly stood as the main driver of the decision of captains to stay or leave a patch, both at Kerguelen and at Crozet, for any level of competition with cetaceans. Daily thresholds of 500 kg and 2,700 kg were suggested as driving the decision of captains to leave a patch at Crozet and at Kerguelen, respectively, i.e. below these masses of toothfish caught during a day, fishers were more likely to leave for another patch. In addition, in the presence of cetaceans, we showed a threshold effect of the fishing success upon the relationship between the number of cetaceans and the decision to stay. From our results, the number of cetaceans had no influence on the captain's probability to leave a fishing area if the fishing success was higher than 5,866 kg of fish caught per day.

Decision making by fishers around Crozet and Kerguelen may reflect a passive adaptive strategy as defined by [START_REF] Clark | Bioeconomic Modelling and Fisheries Management[END_REF], since fishers rely on the constantly updated information provided by the patch to decide whether to leave it or not. Such strategy has been reported to be also used by fishers in more traditional fisheries, resulting in an optimal use of foraging patches [START_REF] Begossi | The use of optimal foraging theory in the understanding of fishing strategies: A case from Sepetiba Bay (Rio de Janeiro State, Brazil)[END_REF][START_REF] Aswani | Patterns of marine harvest effort in southwestern New Georgia, Solomon Islands: resource management or optimal foraging?[END_REF]. This behaviour of optimal foraging has to be contrasted with a long-term sustainable strategy of the resource.

Maximizing catch rates by staying in a locally productive fishing area may be explained by a combination of factors involving both the socio-economic constraints of the fishing activity and the difficulty to find the fish. Vessels allowed operating in the Crozet and Kerguelen EEZs are subject to annual quotas, which are the highest of all Patagonian toothfish fisheries of the Southern Ocean (e.g., 6,300 tonnes shared between the 7 licensed vessels in 2015-2016). The full completion of quotas during a given season ensures the economic viability of the fishing companies, and also demonstrates the capacity of these companies to complete their quotas, which is used to determine the quotas of the following season. In addition, fishery regulations, which are partly defined by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), restrict access to EEZs for a portion of the year and impose both fishing time limits (in days) to local patches within EEZs and minimum fish size policies.

Paired with the costs of fishing operations (fuel, gear, food, crew salaries), the toughness of fishing conditions and the remoteness of the fishing areas (on average vessels spend 3 consecutive months at sea before returning to port, with 7 days steaming each way from Reunion island), these socio-economic constraints lead captains to favour a short-term and local catch rate maximization strategy when finding a productive fishing area meeting all regulations.

This strategy may be further enforced by the specific difficulty to find productive fishing areas since Patagonian toothfish lack a gas-filled swimbladder and detection of fishable numbers can be difficult [START_REF] Foote | Importance of the swimbladder in acoustic scattering by fish: a comparison of gadoid and mackerel target strengths[END_REF]). In addition, the quota allocation between the two localisations is asymmetric: 90% of the quota is set at Kerguelen and the 10% remaining is done at Crozet, and the Kerguelen fishing area is substantially greater than the Crozet fishing area. These features may explain the differences of daily mass threshold (500kg at Crozet and 2700kg at Kerguelen) driving the decisions made by captains. As more fishing effort is required at Kerguelen, captains may seek more productive areas in Kerguelen than in Crozet. Consequently, for captains, a

given fishing success may be acceptable in Crozet but not in Kerguelen.

Influence of cetacean depredation

Although fishing success was found to be the primary driver of decisions by captains, the present study also showed the importance of cetacean depredation on the probability of fishers to stay on or to leave patches. For a given fishing success (i.e. the same mass of fish caught per day), captains were more likely to leave a patch when depredation occurred and when the number of depredating individuals increased. The number of depredating individuals also influenced the rate at which decisions were made: captains took the decision to leave sooner as the number of cetaceans increased. These results highlight the fishers' perception of both sperm whales and killer whales as competitors and, similarly to the findings of Goldstone et al.

( 2005), fishers react to competition by avoidance.

The competition effects of cetaceans underlined in our study is line with previous studies assessing the impact of cetacean interactions on the fishing success of fishers. In Crozet, killer whales and sperm whales were found to be responsible for the removal of 30% of the total catch of toothfish between 2003-2013, whether the two species interacted alone or simultaneously with vessels [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF]. However, killer whales are likely to be considered by fishers as a more serious competitor than sperm whales, since the biomass loss when killer whales depredate alone has been estimated to be twice the biomass loss when sperm whales depredate alone [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF]. In addition, our results suggested that the number of both depredating sperm whales and killer whales may positively influence the probability of captains to leave a fishing area. However, for killer whales alone, this relationship, which was based on a small sample size (n = 78 fishing sessions), was only close to significance in the models. An increased number of depredating killer whales and sperm whales was shown to result in increased amount of depredated fish [START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF] and is, therefore, likely to increase the probability of fishers to leave.

Factors explaining between-captain variability

Our result emphasized high between-captain variability in the probability to leave an area, either as a response to the fishing success or as a response to the competition with cetaceans. In addition, between-EEZs variability per captain of the probability to leave a ground as a response to the fishing success was low, which suggests that captains are consistent in their decision to leave a ground according to a same variation of fishing success wherever they fish.

Together, these results underlie the importance of individual captain personalities over their decision to stay or leave a patch. We observed that the probabilities to leave a fishing area varied differently between captains according to their fishing success, suggesting differences either in fishing strategies or in perception about what is an acceptable level of fishing success.

Within the context of OFT strategies, captains may play upon different technical variables, such as the number of hooks set, the setting depth, the soaking time, the hauling speed and so on.

Nevertheless, differences in quotas between vessels may lead to differences in captains' decision making. For instance, during the season 2014-2015, quotas varied between the seven fishing vessels from 100 to 140 tonnes at Crozet and from 630 to 820 tonnes at Kerguelen (French Antarctic and Austral Territories Prefectural Decree n° 2014-76).

Differences in experience may also play a role in the variation of decision making between captains, either in response to a decrease of fishing efficiency or to the presence of cetaceans. We assume that more experienced captains may know how to react to both situations.

While facing competition, some captains may indeed try to adapt their fishing techniques in presence of cetaceans. They can increase hauling speed or shorten longline length in order to limit the depredation rate both by killer whales (Tixier et al. 2015c) and by sperm whales [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. We may thus assume that fishers who successfully limit the competitive effect of cetaceans may be less sensitive to them. Conversely, less experienced captains who face depredation for the first time may react differently than more experienced captains.

Species may also be a factor determining differences of decision making between captains. Killer whale depredation is more obvious for fishers than sperm whale depredation, as killer whales leave some fish remains on longline. In addition, killer whales are usually observed depredating in larger groups than sperm whales, even though killer whales interact less with vessels than sperm whales. These differences of depredation perception by fishers may make killer whales more annoying to them [START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF]. Furthermore, since killer whales cause greater fish losses than sperm whales, it could explain the null between-captains variability in the decisions when confronted with depredation by killer whales alone compared to the variability observed when confronted with sperm whale depredation alone.

Captains may be differently sensitive to the presence of sperm whales, as we observed high variability of decision between captains in our models while facing sperm whales competition. In addition to lower losses caused by sperm whales, previous studies have shown that sperm whales are naturally distributed on highly productive patches [START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF]). Unlike killer whales, for which it is still not clear whether they naturally feed on Patagonian toothfish, the latter has been confirmed as an important prey item of sperm whales in the Southern Ocean [START_REF] Yukhov | Antarkticheskij Klyklash[END_REF]). As such, the presence of numerous sperm whales could sometimes be associated with rich fishing areas, which may result in a diluted impact of depredation in catch rates remaining high and which may, thus, influence the perception of fishers.

WHAT MAY BE THE CONSEQUENCES OF FISHERS' OPTIMAL

STRATEGIES ON ECOSYSTEMS?

Consequences on local fish resources

Considering that fishers act like optimal foragers, since they aim at maximizing their fishing efficiency, we may wonder if this behaviour could be a risk for the long-term sustainability of local ecosystems, which include both the fish resource and the depredating whale populations. Fishers' choices seem to be only based upon short-term strategies, so there could be a risk of local depletion of Patagonian toothfish on patches. The foraging success clearly stands as the main driver of patch use even in the presence of cetaceans, and until recently the incorporation of the amount of depredated fish has remained limited in quotas allocation processes [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF], Gasco et al. 2015). For instance, considering that 30% of the total catch in Crozet is lost to depredation [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF] and for a given quota of 850 t.year -1 (e.g. season 2014-2015 within the Crozet EEZ), an additional 365 tonnes of Patagonian toothfish are caught but removed by cetaceans. The optimal strategies combined with the depredation may then become an issue to fishing stocks, within the condition that these amounts of depredated fish would not have been eaten in natural condition by cetaceans. We may indeed expect an overexploitation of the resource at the local scale due to the maximisation strategy of captains coupled to the loss due to depredation.

Furthermore, depredation may create bias in the length-frequency distributions used in fish stock assessment procedures since [START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF] revealed that killer whales and sperm whales tend to depredate large Patagonian toothfish. Additionally, fishers concentrate their effort in fish-rich areas, where depredation is more likely to occur, especially in presence of sperm whales [START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF], Gasco et al. 2015). The decision to leave is then less likely to happen in such areas, as our results suggested that for a high fishing success (~6t.day -1 ), fishers stay on a patch due to their optimal foraging strategy. Hence, a resource crunch is to be considered at finer scale than considering an EEZ range or even at a Small Scale Management Unit (1° X 0.5°), for instance at a seamount, a plateau or a slope level.

Consequences on local cetacean populations

Although fishers are more likely to avoid competition with cetaceans, we observed that captains do not leave fishing areas if their fishing success is high, even with an increasing number of cetaceans. Such decision may, therefore, provide cetaceans with large amounts of fish to depredate and, thus, with highly energetic and easily accessible resource in abundance.

The consequences of artificial food provisioning on the sperm whale population has not been yet assessed since this population has been monitored only recently [START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF]).

However, artificial food provisioning from fisheries was shown to result in increased survival and reproduction for the depredating killer whales of the Crozet population (Tixier et al. 2015a, Guinet et al. 2015).

We may also assume that the optimal foraging strategies of fishers reinforce cetaceans' depredation behaviour. If we now consider cetaceans as optimal foragers and depredation behaviour as a cost-effective strategy, due to the low foraging effort and the high energetic intake from toothfish [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF], it is more likely that whales favoured interactions with longlines instead of natural hunting. Depredation may have been learned independently by some killer whale matrilines and sperm whale individuals, and then socially transmitted to other individuals for both species by mimicry [START_REF] Schakner | Using Models of Social Transmission to Examine the Spread of Longline Depredation Behavior among Sperm Whales in the Gulf of Alaska[END_REF]. A cost-effective behaviour is more likely to be spread within a population of highly social cetaceans [START_REF] Rendell | Culture in whales and dolphins[END_REF], especially as apprenticeship is important for cetaceans and relies on both vertical and horizontal cultural transmission [START_REF] Guinet | Intentional stranding apprenticeship and social play in killer whales ( Orcinus orca )[END_REF][START_REF] Ford | Dietary specialization in two sympatric populations of killer whales (Orcinus orca) in coastal British Columbia and adjacent waters[END_REF][START_REF] Deecke | Dialect change in resident killer whales: implications for vocal learning and cultural transmission[END_REF], Whitehead et al. 2004). Thus, killer whales and sperm whales might become more and more dependent of the longliners' activity. An increasing dependency of cetaceans to fisheries might become a concern if whales search for longliners outside the EEZs, where illegal fishing activity is still observed (Thierry Clot, pers. comm., from the French Antarctic and Austral Territories Administration) and is assumed to respond to depredation by cetaceans using lethal techniques.

CONCLUSION

In agreement with human ecological studies using OFT, our study showed that optimal patch modelling is a useful tool to analyse fishing strategies. However, we bring here new evidence of the possibility to monitor modern fishing activity according to the OFT, whereas previous studies focused on more traditional fisheries.

ACKNOWLEDGEMNTS

We are particularly grateful to all the dedicated fishery observers operating from the vessel, and to the Museum National d'Histoire Naturelle de Paris for sharing these data. We would like to thank a lot Dr. J. Arnould for editing the English of this manuscript. We also thank A. Janc for her helpful comments. We are also grateful to the anonymous reviewers who helped to improve the manuscript through their insightful comments. Support was provided by the Terres Australes et Antarctiques Françaises and the Réunion Island fishing companies. The long-term monitoring of killer whales was supported by program 109 (resp. H. Weimerskirch) of the French Polar Institute.

WHAT INTEREST TO ASSESS HUMAN BEHAVIOUR?

CAPTAINS BEHAVIOUR WHILE EXPLOITING A PATCH

Using the optimal foraging theory, I observed that although captains' decision to use a patch is mainly driven by the fishing success, competition with odontocetes can strongly influence their decision (section 2.2). However, this article only focused on captain's prospection behaviour, i.e. setting decision, but it did not assess their foraging behaviour within a patch, i.e. the hauling decision. Thus, we may also wonder whether captains forage optimally within a patch. Thus, I aimed at assessing the decision to haul a longline when a choice is available. An optimal foraging strategy would consist in covering the shortest distance in order to haul the next longline among several ones set on the seafloor. The purpose was then to explain the hauling choice of the next longline for captains according to the optimal foraging theory. Similarly to our article (section 2.2), I hypothesised that the fishing success may influence positively the decision to haul the closest longline whereas the competition with odontocetes may drive away the captains. I thus defined an optimal distance score (ODS) to estimate whether the captain made the most optimal choice by choosing the closest distance to haul the next longline (score =1) or the least optimal choice by choosing the furthest distance (score =0). The optimal distance score was estimated at the end of every longline hauled before another one (without any setting phases in between) and when at least 2 longlines remained on the seafloor, i.e. when a choice was available. Thus, I measured distances between the position of the fishing vessel at the end of the hauling and all extremities of longlines still soaking, considered as all the possible distances (𝑑 𝑝 ). From these values, 𝑑 𝑝 , I kept the minimum and the maximum from which I compared to the real distance (𝑑 𝑟 ) covered by the fishing vessel between the end of the hauled longline and the beginning of the next one. Thus, ODS ∈[0,1]

and was defined as: I assessed the influence of the fishing success and the occurrence of depredation on the optimal distance score. The optimal distance score ∈[0,1] since it represented the proportion of optimality within captains choice. I thus investigated the logistic regression between the proportion to make the optimal choice (π) with the fishing success and the competition [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]). The fishing success was expressed as the mass of toothfish caught on the hauled longline (kg), and the competition was set as a categorical variable with odontocetes occurrence (presence of species and absence of odontocetes). I also assessed the effect of the interaction between both predictive variables: fishing success and competition. Captains were considered as a random effect to investigate variations of decision between them. with πij the proportion to make the optimal decision for a captain j at the end of the hauled longline i, α the intercept, aj the random intercept for captain j, β1,2,3 the coefficients of the three predictive variables , bj the random effect for captain j on the slope of fishing success and ε ij the residual for the hauled longline i and captain j. Besides, using a top-down approach [START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF], I assessed first the most complex model, i.e. with the interaction between the two predictive variables and the random effect (captain j) set both on the intercept (α) and on the slope (β1) of the continuous variable (fishing success) through the Akaike Information Criterion (AIC) selection [START_REF] Akaike | A new look at the statistical model identification[END_REF][START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]). The statistical model was implemented through the software R with the function glmer (package lme4).

According to the AIC selection, only the models for the Crozet EEZ were better than the null model. The best model for Crozet was obtained with the competition as the only explanatory variables (excluding the fishing success) with a random effect of the captains on the intercept (α + aj):

EQUATION 2-13 : logit(πij)= α + aj + β2Competition ij +ε ij
Interestingly, the model selection dropped out the fishing success, thus this variable did not explain the hauling decision of captains. However, the proportion of optimal decision in absence of odontocete was significantly different from 0, i.e. the least optimal choice (πij = 0.79, p<0.001, Figure 2-12), with a significant variability between captains (std=0.27). The optimal distance score decreased in presence of odontocetes with a significant difference between the optimal distance scores in absence of competition and in presence of killer whales (pkw <0.001, These results revealed that captains display an optimal foraging behaviour within a patch since they tend to choose the closest longline whatever the fishing success and the competition.

However, the models highlight that competition generated by the occurrence of killer whales impacts negatively the optimality of captains' hauling decision. This competition effect was not detected for sperm whales. Indeed, no competition effect was perceive in Kerguelen, where only sperm whales are present, as revealed by the mean value (absence in red and presence in dark green) on Altogether, these observations show that competition decreases the optimality of captains decision as captains are either more likely to leave a patch in the presence of cetaceans or more likely to choose a further longline when killer whales are present. However, I did not assess the costs of implementing a non-optimal strategy in response to killer whales.

Nevertheless, as interaction with odontocete may engage captains to cover larger distances than they should have done (optimal distance score<1), this extra distance may be used as a proxy of an induced depredation cost. Thus, the extra distance (𝑑 𝑒 ) covered was measured as the difference between the real distance covered to haul the next longline and the minimum distance with the closest longline set on the seafloor that the captains could have covered: in presence of killer whales alone (Figure 2-13). Thus, in presence of killer whales for instance, when captains chose to not haul the closest longline they cover 51% more distance than in absence of odontocete. This increase of distance begets considerable costs such as a rise of fuel consumption. However, additional fuel consumption is not the only cost that may be generated by the decrease of the fishermen optimality, but an increase of fishing effort, either by time or by fishing gear deployed may result in huge expenses [START_REF] Peterson | Killer Whale Depredation and Associated Costs to Alaskan Sablefish, Pacific Halibut and Greenland Turbot Longliners[END_REF]. Thus, further investigations should assess more precisely some captains' foraging strategies as a combination of the decision to deploy to haul and to use certain fishing practices according to their environment.

CONCLUSION

In summary, this chapter confirms that fishermen behave like optimal foragers as their decisions are driven by variation of the environmental conditions. Captains face indeed decision-making with different implication term. When setting longlines, captains are prospecting for the resource. Their decision to set longline is therefore mostly based upon a long term strategy to find the best resource abundance (section 2.2). Once longlines are set captains could not improve anymore the catchability of the resource. However, during the hauling captains' decision will be important to reduce fishing costs and so captains rely mostly on direct environmental information, such as the presence of competitors (section 2.3.1).

Besides, the behavioural ecology approach also revealed a large variability between captains, suggesting the importance of individual perception. However, further investigation should also assess the effect of captains' experience. Such approach would consist to determine whether captains behave more similarly to a different captain with the same experience than to himself with a different experience. I hypothesise that captains during their first trip behave like naïve foragers. Then with experience fishing strategy should become more efficient and the perception of competition may evolve.

A better understanding of fishermen behaviour is thus essential toward a finer investigation of direct costs due to depredation. These costs could be quantifiedthrough the loss of optimality in captains foraging behaviour. Additionally, such an approach may also bring a new lead for mitigation measures by assessing the most optimal strategy in presence of signatures may explain differences of interaction rates between vessels (Figure 2-1), therefore different acoustic signature through the manoeuvrability of their vessels may explain another part of the inter-captains variability of interaction rates. This assumption was examined using a passive acoustic monitoring approach and results are presented in the following Chapter 3.

PASSIVE ACOUSTIC MONITORING

The previous chapter focused on the interest to study human behavioural ecology to better understand relationship between fishermen and the competition. This approach notably revealed differences of interaction rates between captains on Figure 2-1. This inter-captain variability was partially explained through differences of decision making and by different competition perception, suggesting that captains less sensitive would be less likely to leave in presence of odontocetes and thus would increase their interaction rates. This approach assessed in a way the consequence of the risk of interaction with odontocetes upon captains' behaviour;

however the consequence of captains' behaviour upon the risk of interaction remains unclear.

Using this opposite view of the issue would target how behaviour of captains may increase the risk of interaction, such as through an augmentation of their detectability. An assumption is that differences of detectability could be due to extrinsic signals, related to the vessels' navigation (i.e. captains sailing behaviour) or to intrinsic signals, related to vessels' specificities themselves. Intrinsic signals may then explain the inter-vessels variabilities observed on Figure 2-1. Thus, this chapter focuses on the acoustic signals as an explanation the detectability of fishing activity by odontocetes, through a passive acoustic monitoring approach. In a further extent, this chapter also determines how passive acoustic monitoring could bring a better understanding on the underwater dimension of the odontocete depredation behaviour.

INTRODUCTION

PASSIVE ACOUSTIC MONITORING TO STUDY

DEPREDATION

Stimuli of any kind propagate differently underwater than in the air. Species like cetaceans have sensory and communication systems adapted to a fully aquatic environment (Watkins and[START_REF] Watkins | SENSORY BIOPHYSICS OF MARINE MAMMALS[END_REF][START_REF] Wartzok | Marine Mammal Sensory Systems[END_REF]. As beams of light are scattered and absorbed by water at closer range than in the air, the vision of marine mammals allows them to primarily distinguish their environment only at very close range (Watkins and[START_REF] Watkins | SENSORY BIOPHYSICS OF MARINE MAMMALS[END_REF][START_REF] Wartzok | Marine Mammal Sensory Systems[END_REF]. These predators need therefore to rely on other environmental clues and other senses than vision to detect their preys. One reliable information signal underwater is sound. In the aquatic environment, sound travels five times faster than in the air, circa 1500 m.s -1 instead of circa 300 m.s -1 . Sound can travel from a few metres to a hundreds of kilometres within the oceans depending on their intensity and their frequency, while light usually does not propagate for more than dozens of meters. It is then not surprising that evolution has driven cetaceans to primarily rely on acoustic signals [START_REF] Wartzok | Marine Mammal Sensory Systems[END_REF]. All cetaceans produce communication signals. Pieces of information contained in cetacean communication signals are still poorly understood since a broad range of sounds could be produced across species, contexts, behavioural states, etc. Indeed, signals, either random or stereotyped, can vary from short pulses to multi-themed songs including whistles and calls [START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF]). Some of these sounds are produced to be heard by prey in order to scare them within a foraging purpose.

For instance some population of killer whales and bottlenose dolphins produce feeding-specific calls to manipulate preys and facilitate capture [START_REF] Janik | Food-related bray calls in wild bottlenose dolphins (Tursiops truncatus)[END_REF][START_REF] Simon | Icelandic killer whales Orcinus orca use a pulsed call suitable for manipulating the schooling behaviour of herring Clupea harengus[END_REF]. Another example of feeding-specific sounds but different to calls are produced by humpback whales when bubble-net feeding on herring [START_REF] Cerchio | Variation in Feeding Vocalizations of Humpback Whales Megaptera Novaeangliae from Southeast Alaska[END_REF]. More specific sounds related to feeding and at larger extent to foraging activities are the echolocation signals.

Echolocation is the use of a signal's echoes from an emitting animal to estimate the direction and the range of an object [START_REF] Griffin | Listening in the dark: the acoustic orientation of bats and men[END_REF][START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF]. Echolocation signals, such as clicks, have first been described in bats [START_REF] Griffin | Listening in the dark: the acoustic orientation of bats and men[END_REF], and in odontocetes to localise prey [START_REF] Backus | Physeter clicks[END_REF]Schevill 1966, Zimmer 2011). A click of echolocation is a broad-band sound, for instance circa 100 Hz to 150 kHz depending on odontocetes species, and is produced during a very short duration (i.e. a few to some twenty milliseconds). Altogether, communication and echolocation signals are good acoustic clues to interpret the behaviour of individuals, leading researchers to increasingly use passive acoustic monitoring (PAM) as a tool to study odontocete species Within the context of longline depredation, PAM can be used as a reliable approach to assess the temporal and spatial behaviour of cetaceans around fishing gear. For instance, the deployment of acoustic recorders on longlines may allow a more comprehensive monitoring of the presence of depredating odontocetes, improving therefore accuracy in the estimation of interaction rates [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]. PAM may also be implemented through multiple hydrophone deployments allowing tracking of individuals underwater [START_REF] Mcpherson | Development of Passive Acoustic Tracking Systems to Investigate Toothed Whale Interactions with Fishing Gear[END_REF][START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF], Mathias et al. 2013a). Odontocetes are most likely to rely on acoustic to detect vessels and fishing activity. Thus, another interest to use PAM within a fishing context is to identify the specific sounds that may attract depredating species. Although PAM offers interesting insights, for the last 40 years the field of acoustic in a fishery context has mostly focused on deterrents and harassment devices [START_REF] Shaughnessy | Attempts to develop acoustic methods of keeping cape fur seals Arctocephalus pusillus from fishing nets[END_REF][START_REF] Jefferson | Acoustic methods of reducing or eliminating marine mammal-fishery interactions: do they work?[END_REF], Tixier et al. 2015b[START_REF] Wild | Field trials of an acoustic decoy to attract sperm whales away from commercial longline fishing vessels in western Gulf of Alaska[END_REF]. This was partly explained by unwieldy acoustic recorders with high power and memory consumption, allowing only short recording duration, until the 2000s. With recent technological developments it has become easier to deploy autonomous devices on longlines, which lead to an increased use of the PAM approach to study depredation [START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]). Yet, only a few studies have used this approach to observe and measure depredation [START_REF] Mcpherson | Development of Passive Acoustic Tracking Systems to Investigate Toothed Whale Interactions with Fishing Gear[END_REF][START_REF] Hernandez-Milian | Results of a short study of interactions of cetaceans and longline fisheries in Atlantic waters: environmental correlates of catches and depredation events[END_REF], Thode et al. 2014[START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. Among these studies, the Southeast Alaska Sperm Whale Avoidance Project (SEASWAP) has dedicated extensive efforts on PAM to investigate sperm whale depredation on Alaskan longline fisheries (see review [START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]. This project used PAM through four distinct approaches aiming at: (i) better estimating of depredation rates by using distinctive echolocation sounds by sperm whales [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF], Thode et al. 2014[START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]; (ii) developing localisation and tracking methods of depredating whales from hydrophones arrays deployed on longlines' buoy [START_REF] Thode | Tracking sperm whale (Physeter macrocephalus) dive profiles using a towed passive acoustic array[END_REF], 2005[START_REF] Tiemann | Three-dimensional localization of sperm whales using a single hydrophone[END_REF], Mathias et al. 2013a, 2013b, Thode et al. 2015); (iii) determining acoustic cues produced by fishing boats to determine how sperm whales detect and localise fishing activity [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]; (iv) testing and evaluating potential countermeasures such as acoustic decoys and passive deterrents [START_REF] O'connell | Testing a passive deterrent on longlines to reduce sperm whale depredation in the Gulf of Alaska[END_REF], Thode et al. 2015[START_REF] Wild | Field trials of an acoustic decoy to attract sperm whales away from commercial longline fishing vessels in western Gulf of Alaska[END_REF]. SEASWAP revealed the large potential of PAM to understand, measure and mitigate odontocete depredation [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF] .

Despite this research potential, PAM has never been implemented as a tool to study odontocete depredation on the Patagonian toothfish longline fisheries in the Southern hemisphere. During my PhD, I introduced PAM to the French and Australian toothfish fisheries with the purpose to collect long-term acoustic data from fishing vessels and longlines.

PAM WITHIN THE ORCADEPRED PROJECT

In our depredation study case, we focused on two odontocete species: sperm whales and killer whales. These species both produce echolocation clicks [START_REF] Norris | The echolocation of marine mammals[END_REF][START_REF] Barrett-Lennard | The mixed blessing of echolocation: differences in sonar use by fish-eating and mammal-eating killer whales[END_REF][START_REF] Madsen | Male sperm whale ( Physeter macrocephalus ) acoustics in a high-latitude habitat: implications for echolocation and communication[END_REF][START_REF] Au | Echolocation signals of free-ranging killer whales (Orcinus orca) and modeling of foraging for chinook salmon (Oncorhynchus tshawytscha)[END_REF][START_REF] Miller | Sperm whale behaviour indicates the use of echolocation click buzzes "creaks" in prey capture[END_REF][START_REF] Simon | Echolocation clicks from killer whales (Orcinus orca) feeding on herring (Clupea harengus)[END_REF][START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF], which can be used as a reliable acoustic clue to identify foraging events. Using clues like 'buzzes' or 'creaks', described as an acceleration of click rates [START_REF] Madsen | Echolocation clicks of two free-ranging, oceanic delphinids with different food preferences: false killer whales Pseudorca crassidens and Risso's dolphins Grampus griseus[END_REF]) and found to be associated with prey catch attempts [START_REF] Miller | Sperm whale behaviour indicates the use of echolocation click buzzes "creaks" in prey capture[END_REF][START_REF] Watwood | Deep-diving foraging behaviour of sperm whales (Physeter macrocephalus)[END_REF], we may detect fish removal from longlines [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]. Killer whales, which have a more diverse acoustic repertoire than sperm whales, may also be detected by clues other than echolocation clicks, such as stereotyped pulsed calls and whistles, which they primarily use as communicative signals [START_REF] Ford | Acoustic behaviour of resident killer whales (Orcinus orca) off Vancouver Island, British Columbia[END_REF], 1991[START_REF] Thomsen | On the communicative significance of whistles in wild killer whales ( Orcinus orca )[END_REF][START_REF] Riesch | Stability and group specificity of stereotyped whistles in resident killer whales, Orcinus orca, off British Columbia[END_REF], 2012). These types of signals may therefore be reliable to determine socializing behaviour [START_REF] Ford | Acoustic behaviour of resident killer whales (Orcinus orca) off Vancouver Island, British Columbia[END_REF], 1991[START_REF] Deecke | Dialect change in resident killer whales: implications for vocal learning and cultural transmission[END_REF][START_REF] Miller | Within-pod variation in the sound production of a pod of killer whales, Orcinus orca[END_REF][START_REF] Filatova | Dependence of killer whale (Orcinus orca) acoustic signals on the type of activity and social context[END_REF]). Communication sounds have also been reported in sperm whales but through clicks echolocation sequences, called 'coda' [START_REF] Pavan | Time patterns of sperm whale codas recorded in the Mediterranean Sea 1985-1996[END_REF][START_REF] Rendell | Comparing repertoires of sperm whale codas: a multiple methods approach[END_REF], 2004).

Knowing these species' acoustic repertoire we could monitor their presence and identify their behavioural states around hydrophones deployed on longlines. With acoustic proxies of foraging behaviour, such as clicks of echolocation [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF], we could determine when depredation events start. Aside from answering at which fishing stage (i.e.

setting, soaking or hauling) odontocetes could depredate Patagonian toothfish, PAM might also specify depredation rates during interactions [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF]. PAM may also be useful when depredation is not occurring yet. If the presence of odontocete can be acoustically detected immediately when setting the longlines, this would inform that individuals were already present on the fishing area. Otherwise, if predators are acoustically detected after the setting, PAM can assess their arrival time on longlines. Such pieces of information are crucial to highlight predators' natural distribution. Altogether, we expect to bring new insights on how odontocetes detect the fishing activities. Indeed, we wonder how far vessels could be detected and whether their activities, for instance setting or hauling, influence their detectability. A more direct approach to assess this detectability issue is to monitor vessels' acoustics. We suggest that vessels might be detected either through their own signatures, i.e. intrinsic signals, which could vary between fishing phases [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF], or through sounds induced by captains' navigation, i.e. extrinsic signals. An increase of the acoustic detection range of fishing vessels should increase the risk of interactions with odontocetes. The determination of intrinsic and extrinsic signals is thus crucial to better understand how odontocetes detect fishing activities.

As no acoustic deployment had previously occurred from Patagonian toothfish longliners, the setting of the acoustic monitoring constituted a first important work during this PhD, as presented hereafter. The first step before assessing the objectives to monitor odontocetes and vessels acoustics was to deploy hydrophones. Two different setting was used:

a multi-hydrophones array to allow acoustic tracking and a single hydrophone to allow friendly deployment by fishermen and fishing controllers without scientific oversight on-board. This second setting has then allowed to install a long-term acoustic monitoring from the longliners.

PROTOCOLS AND FIELD METHODS, TOWARD A LONG

TERM MONITORING

FIRST RECORDINGS WITH AN ACOUSTIC ARRAY

Within the purpose to answer when and where depredation occurred, the use of PAM for acoustic tracking is of great value (Mathias et al. 2013a, 2013b, Thode et al. 2015). This approach requires deploying an acoustic array to allow measuring time difference of the sounds' arrival between several hydrophones. A preliminary work was therefore conducted before this PhD to prepare the acoustic tracking tool, described in Appendix 1. The method and results from the models detailed in Appendix 1 were used to determine the best setting for an array of hydrophones. The acoustic array and its mooring were designed by RTSYS, a French acoustic company. This setting was composed of four synchronized hydrophones connected through 100 m cables to a single recorder (model EA-SDA14) as depicted on the Figure 3 From this first experience, we decided to change the acoustic array during a second mission (from end December 2017 to early March 2018). We then used 4 independent selfcontained underwater sound recorders (SoundTrap 300, Ocean Instruments, NZ). The inconvenience was that each recorder has its own base time. We set therefore a pinger (V9-69kHz, VEMCO) between 2 SoundTraps to allow further time recalibration as suggested by Mathias et al. (2013). Each SoundTrap was permanently attached to a small rope which was clipped to the downline below the buoy as depicted on Figure 3-2. Hydrophones were deployed similarly to the first season, i.e. they were associated in pairs at 100 m and 200 m with a space of 10 m between hydrophones of the same pair. However, for this new setting I paired each hydrophone with an 3-axis accelerometer/pressure sensor data loggers (Sextant Technology, New Zealand, see section 4.3) to get information of their position within the water column through the whole deployment. These information will be useful to perform the acoustic tracking since hydrophones positions are essential for the algorithm described in the Appendix1

This new setting was simple to set and to recover as there were no electronic cables between sensors. As a result I deployed the array, composed of the 4 independent hydrophones, 26 times for a total recording duration of 882 h as shown in the Table 3-1.

LONG TERM MONITORING

Within the purpose to assess a long term monitoring of both odontocetes and vessels acoustics, I set an acoustic monitoring program for the whole fleet (section 1.3). In order to be applied on a large scale the protocol disturbance on fishing activities must be minimal. It is therefore essential to have a friendly deployment and an easy monitoring. The longlines are therefore equipped with a single independent hydrophone (SoundTraps), the same as I have deployed during my second mission. A protocol, detailed in Appendix 3, was provided with the hydrophone to the fishery observers before the trip. Within the protocol I have then described how to set the mounting system as showed on Figure 3 Conversely to the French protocol, the recordings are not continuous (15 min on every 30 min)

to avoid filing the intern memory too fast, since data could not be downloaded on-board.

However, PAM around HIMI would allow adding a further level of comparison, i.e. between the two fleets. Indeed, while the two EEZs are adjacent and both experience sperm whale depredation, the frequency of the latter is substantially lower in HIMI than in Kerguelen (Welsford andArangio 2015, Janc et al. 2018). Thus, it might be interesting to assess whether some differences in acoustic between the two fleets exist and if they might explain differences of detectability. Additionally, my protocol is also applied on the French longline fishery operating around Saint Paul and Amsterdam (38° 16′ S, 77° 32′ E, Southern Ocean) and South

East of Australia, both targeting blue eye trevalla (Hyperoglyphe Antarctica) and both impacted by killer whale depredation [START_REF] Tixier | Killer whale ( Orcinus orca ) interactions with blue-eye trevalla ( Hyperoglyphe antarctica ) longline fisheries[END_REF].

Deployments by fishermen and fishery observers started in December 2017 on French longliners and have been covering 3060 h over 89 longlines. As for the Australian longliners, deployments started in August 2017 with 2974 h covered by the hydrophone (but 1487 h of recordings) over 71 longlines. The details of the deployments per vessel are described in Table 3-1. The recording effort should be going as long as possible. The next step would be indeed to integrate the deployment sheet within the fishery observer notebook to facilitate the process and maintain the deployment on the long-term. At least data will be maintain until 2020, and the end of the ANR OrcaDepred.

These recordings have allowed the first recordings of killer whales and sperm whales within the French EEZ around the longliners. At a first sight we recovered some interesting recordings in absence of fishing activity on the equipped longline with killer whales and sperm whales around as their vocalises and clicks are observable on the spectrogram in For instance, a group of killer whales was first visually observed interacting with a longline hauled nearby the one equipped and then the group was encountered again during the hauling of the equipped longline. Between these two hauling sessions the fishermen went further to haul a third longline but with no visual observation of this group. The acoustic recording of this period revealed then that this killer whale's unit staid around the buoy as vocalisations are audible during the whole time. This simplistic example demonstrates that visual observation is not enough to assess presence/interaction of odontocetes around longlines in absence of the fishing vessel. It is not clear whether the group of killer whales or sperm whales heard on the acoustic recording were depredating. However, this presence in the vicinity of longlines may question estimation interaction rates, as it is computed through the visual observation of these species nearby the fishing vessels during hauling. Further investigation should focus on such cases to quantify the foraging activity of individuals around the longline monitored.

Additionally, all these recordings allowed preliminary analyses on vessels' acoustics, presented in this thesis in the next section 3.3. The purpose was to bring some first insights on possible acoustic explanation on differences of interaction rates between vessels and captains (Figure 2-1). These leads would then allow further investigation with more robust statistical analyses on possible cues.

A FIRST APPLICATION: FISHERY ACOUSTIC

VESSELS' ACOUSTIC SIGNATURES

INTRODUCTION

The main component of vessels acoustic is due to the rotation of the propeller blades [START_REF] Ross | Mechanics of Underwater Noise[END_REF][START_REF] Richardson | Marine mammals and noise[END_REF][START_REF] Hildebrand | Anthropogenic and natural sources of ambient noise in the ocean[END_REF]). The sound produced by the propeller is characterized by two components: (i) tonal sounds at specific frequencies which related to mechanical noise from the blade rotation, named the propeller singing; (ii) a broadband noise related to the cavitation as bubbles explode when propeller blades increase the pressure water while rotating, which can be characterized as boiling seawater [START_REF] Ross | Mechanics of Underwater Noise[END_REF][START_REF] Richardson | Marine mammals and noise[END_REF][START_REF] Hildebrand | Anthropogenic and natural sources of ambient noise in the ocean[END_REF]. Sounds of machineries are also part of vessel signature [START_REF] Richardson | Marine mammals and noise[END_REF][START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF]), for instance the hydraulic winch, used to haul the longline, from vessels produce a narrow-band tones [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF]). However, sounds produced on-board do not propagate well underwater and may not cover more than 1 km as observed for the hydraulic winch tones around 200 Hz on longliners in Alaska [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF]).

METHODS

To investigate longliners acoustic signatures, I focused upon the propeller cavitation and singing propellers. I then compared the frequencies of these two types of noise between vessels.

First, I assessed spectrograms over a minute. Second, I assessed the power spectrum (in dB re 1 µPa) to highlight tonal sounds of singing propeller. The power spectrum was estimated through the PAM Guide tool on Matlab [START_REF] Merchant | Measuring acoustic habitats[END_REF] in order to assess the root means square (RMS) with their quantiles. The power spectrum statistics were run per vessels for batched recording files of 3 min when the distance from the hydrophone was estimated around 1 km while hauling. I used 7 and 8 recording files of 3 min for 5 vessels and 3 recording files for the last vessel (because of the quality recording).

In this thesis I provided a preliminary and descriptive result of the analysis of vessels signatures. This aspect, along with the analysis of propagation ranges of the signals produced by vessels, will be treated in depth in the near future. In particular, the determination of detection ranges requires precise information on the distance between fishing vessels and the hydrophones to reliably estimate transmission loss components. Data on vessels trajectories such as AIS would be of great interest; however such dataset was not available during this PhD.

Indeed, AIS vessels data combined to the PECHEKER dataset for longlines [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF] deployed around hydrophones would allow to increase the accuracy of the models. In this thesis distance between the vessel and the hydrophone was estimated from the length of the longlines, knowing on which buoy the hydrophones were deployed. When the vessel was hauling a longline equipped with a hydrophone, I could estimate the distance from the hydrophone using a mean speed of the vessel calculated thanks to the beginning and ending times of the process. I also had access to the PECHKER dataset until the end of my first onboard mission, allowing assessing distances between longlines and hydrophones for the deployments made during the mission 2016-2017.

PRELIMINARY INSIGHTS

Broadband noises of the propellers cavitation of all six longliners concentrate the main energy from circa 100 to 2000 Hz, as observed on the spectrograms and DSP of Figure 3-5 and in Supp. data 4. This is consistent with the frequency range previously described for boats of same size [START_REF] Scrimger | Acoustic source-level measurements for a variety of merchant ships[END_REF][START_REF] Richardson | Marine mammals and noise[END_REF][START_REF] Mckenna | Underwater radiated noise from modern commercial ships[END_REF]. All vessels show from 1 to 3 distinctive ray at around 1 kHz, which stand out on the spectrograms of Figure 3-5 and Supp. data 4. This rays reflect the signals produced by propeller singing [START_REF] Richardson | Marine mammals and noise[END_REF]. However, the numbers of rays paired with their exact frequencies are specific to each vessel (see Figure 3-5 and Supp. data 4). For instance, spectrograms and DPS on Figure 3-5 reveal that vessel #1 has 3 rays which could be confounded as a strong tone around 1 kHz while vessel # 3 shows 2 very distinct tone around 900 Hz and another one above 1 kHz, as depict on Figure 3-5. I thus assume that odontocetes may be able to identify vessels through their distinct features resulting from propeller singing. As different acoustic signatures could propagate at different ranges, we suggest that vessels may potentially have different detectability. Assuming that detectability strongly influence interaction rate, this differences of signatures between vessels may explain some differences of interaction rates between some of them observed on the Figure 2-1 such as the variation of interaction rates with sperm whales between vessels 3 and 8 at Crozet. Indeed, sounds produced at low frequencies usually propagate further than at high frequencies [START_REF] Fisher | Sound absorption in sea water[END_REF]Simmons 1977, Ainslie and[START_REF] Ainslie | A simplified formula for viscous and chemical absorption in sea water[END_REF]. However, the difference of propagation range between a ray at 900 Hz and another one at 1 kHz may not be high, thus only high differences of intensities would increase vessels' detectability. It would then be of interest to monitor the propagation of each tonal sound to estimate whether some vessels might be detected significantly further than other ones. However, the noise level statistics made through a power spectrum varies according to the duration of the recording. To integrate noise level statistics over the appropriate duration, it would require knowing how the hearing systems of these species integrate acoustic signals. Further investigation will focus on this aspect. Also, it would be interesting to assess whether two sister-ships have differences of interaction rates with odontocetes, since these vessels have similar manufacturing characteristics and so should similar signature acoustic. Among the other French vessels some sister-ships exist, but the vessels' identity in the Figure 2-1 are not known preventing to assess interaction rates between sister-ships. Besides, in this preliminary analysis, there are no sisterships among the 6 vessels investigated (Figure 3-5 and Supp. data 4), avoiding the possibility to compare acoustic signatures. However, because the singing propeller varies according to the condition of the propeller, for instance damage propellers may produce more tones and at higher intensities [START_REF] Richardson | Marine mammals and noise[END_REF], it more likely that sister-ships reveal different signatures. [START_REF] Merchant | Measuring acoustic habitats[END_REF]. See Supplementary data for the 3 other vessels.

ACOUSTIC CUES

INTRODUCTION

An acoustic cue could be defined as a signal with a distinct signature associated to a specific navigating behaviour or fishing phase. Although the vessel acoustic signature described through the propeller cavitation and the propeller singing was considered as the main source of detectability (section 3.3.1), an acoustic cue should be an acoustic signal different from this signature. A cue must hold information for the listener and should be recognised through its frequencies components and intensities. In this thesis, a first approach of the behavioural aspect of the detectability of vessels by odontocetes is raised. The purpose was to assess a first description of potential clues over a few fishing events. I therefore examined the variations in the frequency intensities of the signals produced by vessels at 3 different scales: (i) depending on the type of operation: setting vs. hauling; (ii) depending of the state of operation: setting vs.

post setting; (iii) depending of the manoeuvrability during an operation: forward vs. backward.

Additionally, I aimed at determining how far the information held within a cue could reach the odontocetes.

METHODS

I used data from a single vessel to allow for the interpretation of sound differences while avoiding any skew from vessel acoustic signatures. Broadband noise levels were estimated through power spectral densities (PSD, in dB re 1 µPa².Hz -1 ). I used the Welch's method in Matlab to visually compare PSD values and I conducted noise level statistics on PAM Guide [START_REF] Merchant | Measuring acoustic habitats[END_REF] to retrieve the noise values.

I first compared PSD over 15 s of a vessel between a setting and a hauling, from two different recording files but at a similar distance (circa 1 km) from the hydrophone. I then compared over a single recording file, a setting and a post-setting. The PSD of the setting was assessed over 15 s, during which the longline is dropped in the water from the stern of the vessel while the latter heads forward at a high speed (between 6 to 10 knots). The PSD of the postsetting was also assessed over 15 s, 45 s after the setting, i.e. after the last buoy has been dropped off. Lastly, I assessed 30 s of a hauling while the vessel was at 4 km from the hydrophone. The distance between the vessel and the hydrophone was estimated through the longlines positions, accessible in the PECHEKER dataset. During these 30 s the vessel was moving forward and manoeuvred 2 times backward while hauling a same longline. I thus compared the PSD of these two manoeuvres estimated with PAM Guide. I used the two sound samples of the boat going backward batched together of a total duration of 4 s and 2 samples of the boat going forward before and after the two backward events, of the same duration.

I finally aimed at assessing the differences in the propagation ranges of the signals produced by fishing vessels between backward and forward manoeuvres. I used the transmission loss (TL) over the estimated distance between the vessel and the hydrophone to estimate the source level (SL) from the received level (RL) on the hydrophone: The RL was measured as the root mean square (RMS) of the PSD estimated through PAM Guide. I finally resolved the SONAR equation to estimate for which range the TL would reduce the signal to be heard from the ambient noise above a certain threshold (dB):

EQUATION 3-3: SNR= threshold  SL-TL-NL=threshold with NL the ambient noise level estimated through 40 minutes from 7 different recording files when the vessel was between 30 and 50 km, the furthest distances obtained between the vessel and the hydrophone on our recordings. I then considered the ambient noise level as the 50 th percentile of the PSD.

As I used a frequency dependent TL model (Equation 3-2), I assessed the differences of propagation range for different frequencies. I first chose 250 Hz since it seems to represent the higher received sound levels (Supp. data 5) and since low frequencies propagate further than high frequencies. This frequency might thus be the component of the vessel acoustic audible by both species at the furthest range. Indeed, both species should be able to heard this low acoustic vessel frequency since killer whales can produce some pulsed calls around 100 Hz [START_REF] Ford | A catalogue of underwater calls produced by killer whales (Orcinus orca) in British Columbia[END_REF]. Similarly, sperm whales should also hear such frequency (250 Hz), as they are not known to produce clicks of echolocation lower than 200 Hz [START_REF] Backus | Physeter clicks[END_REF]Schevill 1966, Zimmer et al. 2005). I then assessed the propagation range at 1000 Hz, since this frequency has a high energy on the Welch' PSD (Supp. data 5) and is certainly audible by both species. Finally, I assessed the propagation range at 2750 Hz as the vessel produce sound above 2 kHz only when going backward.

PRELIMINARY INSIGHTS

Interestingly, I did not observe any clear difference between PSDs during setting and hauling, especially for frequencies below 2 kHz (Supp. data 6). This first observation confirmed that vessel signatures are mostly composed by propeller sounds (cf. 3.3.1). At both setting and hauling, a strong tone was observed around 1 kHz (Supp. data 6), which matches with the singing propeller signals identified for this vessel (Vessel #1 on Figure 345). Additionally, it may not be able to distinguish the hydraulic winch's acoustic signature since these sounds do not propagate further than 1 km [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF]). Between 2 and 10 kHz PSD values were slightly higher at setting than at hauling. A finer-scale investigation would be required to assess whether the acoustic signals produced by the vessel during setting and hauling propagate on different distance ranges. Also it should be confirmed whether the difference of signals between setting and hauling are actually due to the fishing activity, rather than a possible difference of condition, such as sea state [START_REF] Wenz | Acoustic Ambient Noise in the Ocean: Spectra and Sources[END_REF][START_REF] Hildebrand | Anthropogenic and natural sources of ambient noise in the ocean[END_REF]) since both phases were recorded at different moment.

However, when closely examining the setting phase, I noticed a slight change in the soundscape after the end of setting, at 38 s on the recording (Figure 3456). I noticed indeed that PSD values were slightly higher at setting than at post-setting. This difference was especially visible for frequencies above 2 kHz until 18 kHz, i.e. the maximum frequency recordable, i.e.

half of the sampling frequency (Supp. data 7). As a result, the increase of intensities between 2 kHz and 18 kHz could not be due to the propeller cavitation (signature below 2 kHz, see section 3.3.1). Also, the difference of intensities may be less likely due to a difference of distances with the hydrophone between the two phases. Indeed, with a maximum speed around 10 knots, the boat could not cover more than 230 m in 45 s. I thus assumed that this increase of sound intensity between 2 kHz and 18 kHz may be produced by the longline itself dropping off the boat and hitting the sea surface. This observation may also explain the difference of acoustic signals observed between the setting and the hauling previously described (Supp. data 6).

Consequently, this acoustic difference between setting and post-setting may be an interesting cue for odontocetes to indicate when and where a longline has been deployed. When focusing on manoeuvres during hauling, I observed large differences in both frequencies and intensities between the sounds produced while going forward and while going backward (Figure 3-7 & Supp. data 5). Indeed, the backward manoeuvre produced sounds in broadband frequencies, with the main energy reaching until circa 6 kHz, whereas the main energy of the sound when going forward does not exceed 2 kHz (Figure 3-7 & Supp. data 5).

While on the vessel I have noticed that the hull vibrates more during backward manoeuvres.

The hull might enter in resonance creating these broadband frequencies sounds. This has been confirmed by a French navy expert (Legris personal communication). I estimated the propagation range for two thresholds: 5 and 10 dB re 1 µPa².Hz -1 above the ambient noise level. From the results, the range of detectability of the vessel during a forward manoeuvre was estimated between 7 and 35 km (Table 3-2). This range is consistent with previous studies that did not used acoustics, suggesting that sperm whales and killer whales may lose track of a vessel when the latter travels over at least 30 to 40 km between two hauled longlines [START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]). The backward manoeuvre was found to propagate on a larger distance range, with an estimated range between 30 and 270 km, at 250

Hz (Table 3-2). While a better modelling of the transmission loss (e.g. running beam-tracing acoustic propagation model) paired with accurate ambient noise and whale sensitivity parameters would be required, this preliminary analysis suggested that the sounds produced by a backward manoeuvre propagate circa 4 to 8 times further than those produced by a forward manoeuvre during hauling (Table 3-2). These analyses also show that a predator detecting a vessel from 8 to 18 km could also determine whether it is manoeuvring (Table 3-2). As a result, in addition of being an acoustic cue of the vessel's presence, such manoeuvre could also be a cue of the activity. Indeed, going backward is done sometimes when fishermen miss to catch the buoy or when the longline is stuck and the captain tries to orientate properly the vessels to avoid a break of the longline. Thus, some captains with less experience might manoeuvre more and be therefore more detectable by odontocetes. This hypothesis could partially explain the inter-captains variability of interaction rates for a same vessel, e.g. for vessel 4 at Crozet with killer whales (Figure 2-1). Further investigation should quantify any differences in the sound levels between captains to answer this hypothesis. Nevertheless, going backward seems unusual so other cues must be defined in order to assess any inter-captains detectability. A good candidate might be the approach of the longlines before hauling, which seems to vary according to captains sailing techniques. Indeed, while approaching longlines, fast deceleration or strong direction change is more likely to increase the sound production by the vessels [START_REF] Trevorrow | Directionality and maneuvering effects on a surface ship underwater acoustic signature[END_REF]). Anyway, these preliminary results would suggest that avoiding too many manoeuvres, such as going backward, when it is possible should reduce the risk of vessels' detectability.

CONCLUSION

PAM is an interesting tool to assess the interaction between odontocetes and fishermen, as it allows observing what we can't see. In this chapter, I exposed some preliminary analyses on how PAM may bring insights on interaction at the "fishermen-odontocetes" scale through the vessels acoustics for the Patagonian toothfish fishery. The results are encouraging but the amount of data analysed is too small to draw strong conclusion. It is therefore important to consider these results as interesting leads to follow for further investigations. Whereas previous studies have shown interesting results while focusing on vessels acoustic during hauling [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF], here I have brought a new hypothesis that the acoustic signals during setting might play an important informative role for odontocetes on fishing activity detection.

Assessing precisely how far and based on which cues predators detect the fishing activity could bring important leads toward appropriate countermeasures. A reduction of vessels' detectability may indeed decrease the risk of interaction with these odontocetes. Additionally, using the longlines as support for hydrophones could also bring fine insights on the interaction at the "longlines-odontocetes" scale through the odontocetes acoustic monitoring [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]. This approach has been only presented through example of odontocetes acoustic recording as no analyses has been processed yet. However, the example shows the interest to assess PAM on our dataset, especially to affine the estimation of interaction rates, which is very likely to be underestimated, as revealed with the same method in Alaska [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF].

The acoustic analyses have only been exploratory as the setting of the PAM on this fishery has constituted an important part of this thesis. The process has been laborious but fruitful I have collected around 1538 h of recordings with the acoustic array, which should allow to test and implement the tracking tools presented in the Appendix 1. Also these efforts have allow to set a friendly deployment process from longliners, which has already provided 4547 h of recording, collected by fishermen and fishery observers. Besides, the OrcaDepred funding is running until 2020, the deployments will pursue during this period and should considerably increase the dataset. Next effort should then focus on the application of an automatic detector (e.g. Deteclic see summary in Appendix 2) over this growing dataset to assess the presence of odontocetes around longlines and allow robust conclusions. With a long term monitoring a temporal dimension may also be add to further analyses. As for the vessel signatures, it is expected to cover all longliners and cover also most of the captains still in activity. Such dataset would allow completing the investigation upon the inter-captain variability of interaction rates, through the acoustic approach.

BIO-LOGGING

The previous chapter used a passive acoustic approach to assess the underwater dimension of the depredation. Within this purpose, a preliminary analysis was conducted on the fishing activity detectability, and some methods were discussed to show how PAM, while assessing odontocetes' acoustic, should bring in the future fine insights on the interaction at the "longlines-odontocetes" scale. Within the same purpose, this chapter aims at assessing when,

where and how depredation occurs on the longline through a bio-logging approach. A first biologgers analysis is described in this chapter with a double perception of the interaction at a very fine "longlines-odontocetes" scale, since loggers were deployed on both odontocetes and on fishing gears. Additionally, this chapter proposes an experimental design for new loggers which would bring more insights on the foraging behaviour of odontocetes, either natural or in the depredation context.

INTRODUCTION

A good understanding of the odontocetes' foraging behaviours when interacting with longlines and when naturally foraging are needed to comprehensively assess the depredation conflict. Indeed, this comparison between both situations may reveal how opportunities to depredate modify the behaviour of odontocetes. Simultaneously, a fine-scale monitoring of odontocetes' behaviour in presence of fishing activity should assess their interaction behaviours and may bring more insight on how they detect vessels and longlines, and more precisely how they remove the fish from gears. To answer questions such as when, where and how odontocetes remove the toothfish from the fishing gears is essential to target efficient mitigation measures.

Investigating the fine-scale behaviour of odontocetes is challenging because of the inaccessibility of their environment. To overcome this issue, scientists have developed a broadrange of bio-logging tools. While the first time depth recorder was created in the 1960s [START_REF] Kooyman | Techniques used in measuring diving capacities of Weddell Seals[END_REF], the bio-logging technology has substantially evolved and been made broadly available over the last 30 years [START_REF] Ropert-Coudert | Diving into the world of biologging[END_REF][START_REF] Mcintyre | Trends in tagging of marine mammals: a review of marine mammal biologging studies[END_REF]. Bio-loggers have allowed a better understanding of predators' spatial use of their environment either by following their horizontal displacements through ARGOS and GPS technologies or by assessing their vertical movements within the water column through time depth recorders (for examples see reviews [START_REF] Ropert-Coudert | Diving into the world of biologging[END_REF][START_REF] Mcintyre | Trends in tagging of marine mammals: a review of marine mammal biologging studies[END_REF]. Then, with technological advances, devices have progressively decreased in size and increased in life-span and precision of the sensors. These improvements allow now fine assessment of marine predators foraging behaviours. For instance, the emergence of accelerometers, which measure orientation and movement dynamics of predators, have allowed measurements of fine temporal and spatial scale of predators' movements [START_REF] Shepard | Identification of animal movement patterns using tri-axial accelerometry[END_REF][START_REF] Brown | Observing the unwatchable through acceleration logging of animal behavior[END_REF]. Indeed, from fine movements monitoring it is possible to assess sharp acceleration and to determine feeding events of marine predators [START_REF] Viviant | Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers[END_REF][START_REF] Gallon | Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers[END_REF][START_REF] Ydesen | What a jerk: prey engulfment revealed by high-rate, super-cranial accelerometry on a harbour seal (Phoca vitulina)[END_REF][START_REF] Volpov | Identification of Prey Captures in Australian Fur Seals (Arctocephalus pusillus doriferus) Using Head-Mounted Accelerometers: Field Validation with Animal-Borne Video Cameras[END_REF]. However, these new device loggers are also memory consuming and must be recovered, which is difficult for some marine predators, especially cetaceans. Indeed, the main studies on marine mammals have been conducted on pinnipeds [START_REF] Ropert-Coudert | Diving into the world of biologging[END_REF][START_REF] Mcintyre | Trends in tagging of marine mammals: a review of marine mammal biologging studies[END_REF]) since these species are partially terrestrial making the deployment and recovering easier than for cetaceans who are accessible only when breathing. In addition to this limited accessibility, the limitation in the use of loggers on exclusively aquatic species has been the attachment techniques [START_REF] Hooker | Diving and ranging behaviour of odontocetes: a methodological review and critique[END_REF]Baird 2001, McIntyre 2014). Two techniques are currently used: a long-term fixation, using stainless steel barbs to penetrate the skin, preventing tags recovering, and short-term fixation using suction-cups and allowing tags recovering. There is thus a trade-off between the quantity and the quality of data attainable for cetaceans.

However, satellite transmission of data is possible, which allviates the need to retrieve loggers.

Although the amount of information that can be successfully transmitted is limited, previous implementations have included the execution of simple algorithms capable of extracting basic behavioural metrics such as dive duration or maximum depth. The next step in bio-logging is to implement within loggers some methods developed by the community in order to transmit summarized information from these large data-sets. A recent study assessed this new possibility of transmition by satellites the number of prey capture events made by southern elephant seals during their trip at sea from an on-board processing of accelerometry [START_REF] Cox | Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators[END_REF]). This method is highly promising for other marine mammals' studies, especially cetaceans, but the method should be tested for other species since the algorithm implemented on the tag has been developed for elephant seals [START_REF] Vacquié-Garcia | Predicting prey capture rates of southern elephant seals from track and dive parameters[END_REF], Richard et al. 2016[START_REF] Cox | Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators[END_REF].

Although the number of bio-logging studies is constantly increasing (McIntyre 2014), only a few studies have used bio-loggers to study marine mammals depredation behaviour [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF], Straley et al. 2014[START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF]. The limited number of logger deployments to study this issue is easily explained by the technical and logistical constraints imposed by the fishing context. Indeed, odontocetes do not necessarily approach vessels with a range close enough to allow tagging. Moreover, during hauling, vessels are not manoeuvrable to help for tagging. First biologging studies on depredation have described some sperm whales interaction behaviours: sperm whales following fishing boats [START_REF] Straley | Depredating sperm whales in the Gulf of Alaska: local habitat use and long distance movements across putative population boundaries[END_REF]) and changing diving behaviour with shallow dives while depredating on hauled longlines [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF]. Biologging studies are encouraging to study more species in different fisheries as they allow for data to be collected during interaction phases when individuals are sighted close to the boat (<500m, see [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF]) but also during potential interaction phases occurring when the boat is away from the fishing gear. Fine spatio-temporal scale of marine mammals movements still require more investigation to highlight how animals spot fishing activities and to improve our understanding of interaction behaviours. Another interesting use of loggers to investigate marine mammals' depredation is by deploying loggers on longlines, which is much easier than tagging marine mammals. For instance, accelerometers deployed on pelagic longlines paired with video cameras have revealed that odontocete depredation may occur not only on the catch, but also on the baits [START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF]. Camera deployment is more complicated on demersal longlines because of low light conditions. Preliminary trials have revealed depredation by southern elephant seals on Patagonian toothfish fishery during soaking time (van den Hoff et al. 2017) although the seals might have been attracted by the light explaining the fortunate video footages. Thus, deployment of accelerometers on demersal longlines should be more workable than camera, similarly to the first deployments on pelagic longlines in Hawaii [START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF].

In this thesis I used both animal-borne behavioural data loggers and longline-attached data loggers to assess the interactions between the odontocetes and the fishing gear. My contributions are two-fold: both methodological and experimental. On the first hand, I assessed a method to estimate prey catch attempts by large odontocetes to be embedded within a logger for on-board processing (section 4.2). The method was assessed using DTAGs' data from SMRU (Dr. Johnson The number of loggers deployed on odontocetes may look relatively small, when compared to the length of the field mission (3 months). However, the number of loggers was limited because of the costs of the loggers (10 available), and opportunities were quite rare (8 deployments). Indeed, a successful deployment requires gathering good conditions both from the weather and the approach of individuals. As deployments are made by crossbow from the deck at 5 m above the water (without any swell), individuals should swim along the hull to enable a good shot with enough power to peg the tag in individuals' skin. On the one hand, although sperm whales were seen quite often (present around 75% of my mission, both at Crozet and Kerguelen) they did not approach close enough. On the other hand, although killer whale came close enough to the boat, they were not seen very often (observed 10 days during my mission at Crozet). My experience confirmed the difficulty to deploy loggers from fishing boats, but the success on some killer whales is encouraging for further deployments. However, new deployments would require rethinking the material to optimise success, based upon this experience on such condition.

For the accelerometer/pressure loggers attached on the swivel of a hook (see description on section 4.3.3 and Supp. data 8), deployments were found easy to process directly on the operational fishing gear. Deployment did not require any intervention of the fishermen, as the hooks equipped with loggers were baited as the other hooks. The recovery was also easy but necessitated the help of the fishermen, as they removed the loggers from the mainline while hauling the fish. However, the recovery was a little bit time consuming and for the sake of fishermen's work it was preferable to not deploy too many loggers on the same section. The deployment could be constraining when the longline had to be hauled fast (for instance in presence of odontocetes) or within bad weather conditions, increasing then the risk to lose or to miss a logger and damaged it through the winch. As the deployment of these loggers did not required to set experimental sections of longline, the number of deployments could be sizable.

Thus 556 hooks were equipped over the two missions. However, although the number of loggers is important, it is questionable whether it is representative of the whole fishing effort and thus whether such effort could quantify what is happening underwater, which would be approached in section (section 4.3).

ON-BOARD PROCESSING ACCELERATION DATA TO

ASSESS PREY CATCH ATTEMPTS

INTRODUCTION

The purpose of this first study was to test a method to estimate prey catch attempts from acceleration data, similarly to the methods implemented on pinnipeds [START_REF] Cox | Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators[END_REF]. I used data from DTAGs, combining acoustic recording with acceleration data [START_REF] Johnson | A digital acoustic recording tag for measuring the response of wild marine mammals to sound[END_REF]. Sperm whales use echolocation to forage, and more precisely, they produce "buzzes", which have been described as click trains with high click rate, while attempting to catch a prey [START_REF] Madsen | Echolocation clicks of two free-ranging, oceanic delphinids with different food preferences: false killer whales Pseudorca crassidens and Risso's dolphins Grampus griseus[END_REF][START_REF] Miller | Sperm whale behaviour indicates the use of echolocation click buzzes "creaks" in prey capture[END_REF][START_REF] Watwood | Deep-diving foraging behaviour of sperm whales (Physeter macrocephalus)[END_REF][START_REF] Fais | Sperm whale predator-prey interactions involve chasing and buzzing, but no acoustic stunning[END_REF]. Indeed, [START_REF] Fais | Sperm whale predator-prey interactions involve chasing and buzzing, but no acoustic stunning[END_REF] observed through acceleration data that sharp movements occurred at the end of buzzes, which may revealed the prey engulfment. Thus, buzzes are good acoustic clues for prey catch attempts. While this previous study focused at the scale of buzzes [START_REF] Fais | Sperm whale predator-prey interactions involve chasing and buzzing, but no acoustic stunning[END_REF], I wonder whether acceleration only would enable estimation of the number of prey catch attempts per dive. Our purpose was to assess a simple model to estimate sharp movements, which could be easily implemented within a tag. I then required a model with two constraints. First, acceleration would be processed at a dive scale and not over the whole logger dataset. Second, the method must be fully automatic, i.e. it must determine a threshold to estimate whether acceleration values define a prey catch attempts. Finally, I tested the accuracy of our method by assessing the correlation between our estimation of prey catch attempts from acceleration data and the number of buzzes, another good proxy of feeding events.

METHODS

PREY CATCH ATTEMPTS ESTIMATIONS

Acceleration data were provided by Dr. Mark Jonson (SMRU, St Andrews, UK) and were obtained from DTAGs deployed on 6 sperm whales in Mediteranean sea. The DTAG is an archival device that records acceleration at 25 Hz, pressure at 1 Hz and acoustic data with sampling-rate programmable between 2-200 kHz [START_REF] Johnson | A digital acoustic recording tag for measuring the response of wild marine mammals to sound[END_REF]. DTAGs were attached on sperm whale body thanks a square array of 4 suction cups and after the release of the tag from the whale, a VHF radio within the tag aided recovering the tag. A dive was considered for a depth below 50 m. For every dive, buzzes were counted thanks to acoustic recording by the DTAG. The acoustic recordings were process by Dr Mark Johnson and he directly provided us the buzzes' timings.

During buzzes made by sperm whales, it has been described through acceleration jerks that prey captures or maybe prey engulfment last around 1.5 s [START_REF] Fais | Sperm whale predator-prey interactions involve chasing and buzzing, but no acoustic stunning[END_REF]. Using a similar approach implemented on pinnipeds, I assessed the standard deviation of acceleration [START_REF] Viviant | Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers[END_REF][START_REF] Gallon | Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers[END_REF]) every second within a moving window lasting twice the duration of the jerk event ( 3 s) described by [START_REF] Fais | Sperm whale predator-prey interactions involve chasing and buzzing, but no acoustic stunning[END_REF]: This approach allowed an easy automatic threshold determination per dive to assess the PCA Once the PCAs were estimated, I monitored their occurrence at the same time as buzzes on sperm whales' dive profiles. I then assessed whether both events are associated. They were considered as associated if the PCA occurred within 10 s before the end of the buzz and 2 s after the end of the buzz.

Data were processed through the software Matlab (version R2015, The MathWorks, Natick, MA, USA).

RELATIONSHIP BETWEEN PREY CATCH ATTEMPTS AND BUZZES

To evaluate the relevance of our PCA detection method, we assessed the linear regression between the number of buzzes Bij and the number of PCA Pij for every individual i and dive j:

EQUATION 4-2 Bij = α +aj+(β+bj)Pij +ε ij
with α and β the global intercept and slopes of the regression, aj the random effect on the intercept for the individual j, bj the random effect on the slope and εij the residual for a given dive i of the individual j. The random effect was tested first on the intercept only (bj = 0), meaning that the regression is the same between individuals but there might be overestimation or underestimation of the number of PCA due to the accelerometer. I then tested the random effect also on the slope (bj ≠ 0), to see if the relation between the number of buzzes and the number of PCA is random, and so the method could not be implemented. After checking the normality of residuals, I compared the linear model to the null model and assessed a step backward approach to determine the best model (with or whithout random effect) through the Akaike Information Criterion (AIC) selection [START_REF] Akaike | A new look at the statistical model identification[END_REF][START_REF] Zuur | Mixed effects models and extensions in ecology with R[END_REF]. The mixed linear models were assessed with a Gaussian distribution, using the function lmer of the package lme4 through the software R (R Development Core Team 2015).

Once a proper model is determined, the regression should allow approximation of the number of buzzes from the estimated PCAs. Indeed, the purpose is to implement this method within a tag without any acoustic data. Thus, I processed to a leave-on-out cross validation with the 6 individuals. I ran the model by removing one individual, such as his numbers of buzzes per dive were unknown. For this individual I then estimated the number of PCA using acceleration data only. Then, the coefficients of the model obtained with the 5 other individuals were used to find back the number of buzzes for the 6 th individual from its number of PCA. I finally calculated the difference between the accurate number of buzzes of this individual and the number of buzzes estimated by the regression from the 5 other individuals. I repeated the process for each individual.

Finally, I assessed whether the PCA determined through the acceleration data occurred at the same time than the buzzes. I measure a percentage of association for both proxies compared to the other one. In other word I estimated the proportion of PCA produced within the vicinity of a buzz and conversely, the proportion of buzzes occurring near PCA. I set this 2 s flexibility within the hypothesis that the acceleration may detect the prey engulfment after a catch and that sperm whale may stop buzzing instantly once the prey is caught.

RESULTS

PREY CATCH ATTEMPTS ESTIMATIONS

The number of prey catch attempts was estimated for 31 dives recorded among the 6 tags. I observed that the distribution of d, for all the dives and all individuals, followed a the same quasi-normal distribution with some over-dispersion (Figure 4-2). The threshold estimated as the absolute of the minimum dispersion value (red line Figure 4-2), allowed us to determined prey catch attempts over the diving profile (red points Figure 4-2). As I added buzzes on the diving profile (green points Figure 4-2), I observed that most of PCA and buzzes overlapped (Figure 45). I finally tested whether the observation of these overlaps between our estimation of PCA and the buzzes are consistent over every dive for every individual.

RELATIONSHIP BETWEEN PREY CATCH ATTEMPTS AND BUZZES

The residuals followed a normal distribution and according to the AIC selection the best regression was found for the linear mixed model assessing a random effect on the intercept only (Table 4-1). The regression between the number of buzzes and the number of PCA was significantly positive (p<0.001, Figure 4-3), and the intercept of the model is not significantly different from 0 because of the variance due to individuals (Intercept= 2.9 ± 6.2). The differences of intercept between individuals suggest that the model may underestimate or overestimate the number of buzzes from a same estimation of PCA between tags. However, the relationship between the number of buzzes and the number of PCA was the same between individuals since adding a random effect on the slope did not improve the regression. As a result, I only have to take into account the intercept variance between individuals to correct the estimation of buzz using the number of PCA determined. I then estimated the number of buzzes for each individual with a regression calculated from the 5 other individuals (Figure 4-4). The application of the regression coefficients obtained with 5 individuals on the PCA numbers of the 6 th individual approximated well the accurate number of buzzes (Figure 4-4, Table 4-2), except for one individual (ID 6) for which I estimated a number of buzzes twice higher than the accurate number of buzzes (Table 4-2). However, for the other individuals I underestimated or overestimated the number of buzzes by 15% of the accurate number (i.e. ~ 5 buzzes, see Table 4-2). I estimated per dive that 51±14 % of the PCAs were associated with a buzz, and the minimum percentage of association for a dive was observed at 30% for and the maximum was obtained at 73 % (Figure 45). Conversely, 61±15 % of buzzes were associated with a PCA, and the minimum percentage of association for a dive was observed at 36% for and the maximum was reached 100 % (Figure 45).

DISCUSSION

Our study assessed by a simple way the prey catch attempts (PCA) using only acceleration data. The method described and implemented on pinnipeds [START_REF] Viviant | Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers[END_REF][START_REF] Gallon | Identifying foraging events in deep diving southern elephant seals, Mirounga leonina, using acceleration data loggers[END_REF][START_REF] Cox | Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators[END_REF]) could be also implemented on sperm whale and probably on other large cetaceans. Indeed I observed a good relationship between the PCA estimated by the acceleration data and the buzzes. The method also brought an easy threshold determination of the prey catch attempts on the acceleration data, allowing then a simple automation within a logger for on-board processing. However, I obtained more PCA than buzzes and 49% of them were not associated to buzzes, i.e. false positives, which may be explained by other fast body movements or by attempts that occur without buzzes. Indeed, sperm whales might not use buzzes for every feeding attempts as they may only accelerate rhythm of clicks production while foraging, but not fast enough to be considered as a buzz [START_REF] Madsen | Male sperm whale ( Physeter macrocephalus ) acoustics in a high-latitude habitat: implications for echolocation and communication[END_REF]. By listening to the acoustic data from the DTAGs it would be possible to define whether the rhythm of clicks accelerates without ending in a buzz to confirm our hypothesis. Thus, either this PCA method may be more precise than the estimation of buzzes, or a lot of false alarms are detected.

However, not all buzzes were associated with PCA suggesting either a miss of the feeding attempt occurred during these buzzes or the body movement to catch the prey was not sharp enough to be detected through our method.

Further investigation should focus on other proxies to estimate prey catch attempts in order to assess different kind of movements realised by the odontocetes. The purpose would be to allow discrimination between acceleration signals which components belong to which movement behaviours. This may allow describing different feeding attempts as individuals hunt differently according to the kind of prey. Nevertheless, the implementation of this method seems interesting enough to assess sperm whales foraging activities as results showed that it would be possible to apply correction coefficients from the linear regression used to approximate the number of buzzes. Although the feeding attempts might be overestimated or false alarms could be detected, the results suggest that we may at least interpret with confidence variation of PCA between dives for same individuals as a variation in feeding activity.

Additionnaly, different prey types may lead to different ratios between PCA and buzzes too.

One limitation of this study could be the difference of diet between sperm whales equipped with DTAG and individuals in Crozet and Kerguelen waters. Thus, further investigations on other populations with closer ecology than in French sub-antarctic islands should be of great interest to confirm the results described in this chapter. However, within the context of our project on depredation, such loggers implanting this method would allow to assess differences of rapid body movements as a proxy of individulas' foraging activity in presence or absence of fishing gear.

The method has been implemented in new loggers by Wildlife Computers and they were available for the second field mission. Unfortunately, none of the 4 loggers deployed on sperm whales transmitted any signal, avoiding the possibility to collect any of these data. Hopefully, the tampering of these loggers should be corrected by Wildlife Computers and new loggers should be deployed in the future. Nevertheless, other more classical ARGOS tracking loggers have been deployed on killer whales and data have been recovered. These bio-logging data have then been analysed paired with the dataset obtained from loggers deployed on longlines. The results revealed new insights of the underwater interactions between toothed whales and fishing gears, as presented hereafter.

EVIDENCE OF DEEP-SEA INTERACTIONS BETWEEN TOOTHED WHALES AND FISHING DEMERSAL

LONGLINES.

Submitted the 20 th of July 2018 in Ambio , under review Gaëtan Richard

ABSTRACT

Toothed whales (odontocetes) feeding on fish caught on hooks in longline fisheries is a growing issue worldwide. The substantial impacts that this behaviour, called depredation, can have on the fishing economy, fish stocks and odontocetes populations raise a critical need for mitigation solutions to be developed. However, information on when, where and how odontocete depredation occurs underwater is still limited, especially in demersal longline fisheries (fishing gear set on the seafloor). In the present study, we investigated depredation by killer whales (Orcinus orca) and sperm whales (Physter macrocephalus) on demersal longlines in the French Patagonian toothfish fishery (Southern Ocean). Using a combination of animalborne behavioral and longline-attached data loggers, we revealed that both species are able to depredate longlines on the seafloor. This study suggests, therefore, that odontocete whalelongline interaction events at depth may be unrecorded when assessing depredation rates from surface observations during hauling phases only. This result has implications for the management of fisheries facing similar depredation issues as underestimated depredation rates may result in unaccounted fish mortality in fish stock assessments. Therefore, while further research should be conducted to assess the extent of deep-sea whale-longline interaction events during soaking, the evidence that depredation can occur at any time during the whole fishing process as brought by this study should be considered in future developments of mitigation solutions to the issue.

Keywords: depredation, killer whales, sperm whales, demersal longlines, Patagonian toothfish, bio-logging

INTRODUCTION

The intensification of fishing activity over the last decades has been associated with an increase in direct interactions between fisheries and marine top predators worldwide [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Read | Bycatch of Marine Mammals in U.S. and Global Fisheries: Bycatch of Marine Mammals[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. Depredation, which occurs when marine predators remove or damage fish from fishing gear, is a type of interaction often resulting in substantial impacts on fishing activity, depredating species and fish stocks [START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]. Longlines are composed of a main line with baited hooks attached at intervals by means of branch lines called snoods. The main line is either deployed in the water column, i.e. pelagic longlines, or on the seafloor, i.e.

demersal longlines. Therefore, longlining is a fishing technique that makes caught fish easily accessible for depredating animals. It has been reported to be the fishing technique most impacted by depredation, especially by toothed whales, i.e. odontocetes [START_REF] Northridge | Marine mammal interactions with fisheries[END_REF][START_REF] Donoghue | Report of the workshop on interactions between cetaceans and longline fisheries, Apia, Samoa[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF]. Indeed, at least 31 species of odontocetes have been reported to interact (either through depredation or bycatch)

with longline fisheries worldwide [START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Depredation on fisheries leads to greater costs for fisheries. This is due to damaged fishing gear, damaged fish losing economical value, and increased effort to both avoid competition and reach quota limits [START_REF] Peterson | Whale interactions with Alaskan sablefish and Pacific halibut fisheries: surveying fishermen perception, changing fishing practices and mitigation[END_REF][START_REF] Peterson | Killer Whale Depredation and Associated Costs to Alaskan Sablefish, Pacific Halibut and Greenland Turbot Longliners[END_REF]Tixier et al. 2015c;[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. For odontocetes, interactions with longlines may increase risks of mortality, either by entanglement in fishing gear, i.e. bycatch [START_REF] Northridge | An updated world review of interactions between marine mammals and fisheries[END_REF][START_REF] Trites | Competition between fisheries and marine mammals for prey and primary production in the Pacific Ocean[END_REF][START_REF] Read | Bycatch of Marine Mammals in U.S. and Global Fisheries: Bycatch of Marine Mammals[END_REF][START_REF] Hamer | Odontocete bycatch and depredation in longline fisheries: a review of available literature and of potential solutions[END_REF], or by the use of lethal methods by illegal fisheries to eliminate competitors [START_REF] Poncelet | Population dynamics of killer whales (Orcinus orca) in Crozet Archipelago, southern Indian Ocean: a mark recapture study from 1977 to 2002[END_REF]Guinet et al. 2015). Also, depredation often involves access to new and easy-to-catch prey resource for predators, which may modify both the energy balance of odontocetes and the natural predatorprey dynamics of local ecosystems [START_REF] Trites | Competition between fisheries and marine mammals for prey and primary production in the Pacific Ocean[END_REF][START_REF] Northridge | Marine mammal interactions with fisheries[END_REF]Boyd 2002;Guénette et al. 2006;Morissette et al. 2012;[START_REF] Tixier | Demographic consequences of fisheries interaction within a killer whale (Orcinus orca) population[END_REF]. For fish stocks, depredation may result in biased assessments and over-exploitation if the amount of depredated fish is not precisely estimated [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF]). Together, these multiple impacts of depredation, may jeopardize the sustainability of local fishing activity. Currently, there is a critical need for mitigation solutions. However, when, where and how fish are removed from longlines by odontocetes remains poorly understood yet is crucial to developing effective countermeasures to depredation. For instance, some mitigation devices may be efficient only during specific fishing phases, such as hauling. This is the case of the 'Cachalotera', a floating net sleeve sliding down over individual fish caught on a hook when the longline is hauled to protect it from depredating whales (Hucke- [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Moreno | Significant conservation benefits obtained from the use of a new fishing gear in the Chilean Patagonian toothfish fishery[END_REF]. Another example is the SAGO, a catching pod going down the longline to collect the fish during hauling [START_REF] Arangio | Minimising whale depredation on longline fishing[END_REF].

Depredation has been described to occur during both soaking and hauling phases for pelagic longlines (Dalla [START_REF] Rosa | Killer whale (Orcinus orca) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions[END_REF][START_REF] Forney | What's the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries: Patterns of cetacean bycatch and depredation in Hawaii[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF][START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF]) whereas this behavior has only been assumed to occur during hauling phases for demersal longlines (e.g. [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. In dermersal longline fisheries, depredation rates are assessed from visual observations of depredating animals in the vicinity of fishing boats, or counting the number of damaged fish on hauled longlines (e.g. [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Purves | Killer whale (Orcinus orca) and sperm whale (Physeter macrocephalus) interactions with longline vessels in the Patagonian toothfish fishery at South Georgia, South Atlantic[END_REF][START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF], Straley et al. 2015[START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF].

However, some studies using alternative types of data, such as underwater videos or acoustics, have showed that odontocetes can depredate baits and remove the whole fish from the hook [START_REF] Mathias | Relationship between sperm whale (Physeter macrocephalus) click structure and size derived from videocamera images of a depredating whale (sperm whale prey acquisition)[END_REF][START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF][START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF]. Therefore, methods using indirect approaches have been implemented to assess the extent of depredation in demersal longline fisheries. For instance, [START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF] proposed to estimate depredation rates based on the difference in fishing efficiency in the absence and in the presence of odontocetes within a restricted geographical area. Such approach requires an accurate and reliable monitoring of the presence of depredating animals during the fishing operations. However, there are still large knowledge gaps on the underwater depredation behaviour of odontocetes. Specifically, it is not known if the odotoncetes depredate on demersal longlines soaking on the seafloor while the fishing vessel is potentially hundreds of kilometres away. Although interacting with longlines on the seafloor would require individuals to dive deeper and, therefore, to expend greater energy than when depredating on longlines in the water column, both killer and sperm whales have diving capabilities that fully or partly encompass the depth range at which longlines are set on the seafloor. Indeed, killer whales have been recorded diving at maximum depths circa 1000 m [START_REF] Reisinger | Movement and diving of killer whales (Orcinus orca) at a Southern Ocean archipelago[END_REF][START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF] and sperm whales at maximum depths of circa 2000 m [START_REF] Teloni | Shallow food for deep divers: Dynamic foraging behavior of male sperm whales in a high latitude habitat[END_REF][START_REF] Fais | Sperm whale echolocation behaviour reveals a directed, prior-based search strategy informed by prey distribution[END_REF][START_REF] Guerra | Diverse foraging strategies by a marine top predator: Sperm whales exploit pelagic and demersal habitats in the Kaikōura submarine canyon[END_REF]).

In the present study, we approached this problem by using fine-scale bio-logging technology deployed on both depredating odontocetes (ARGOS satellite tracking tags equipped with depth sensors) and longline sets (accelerometers paired with depth sensors) from commercial demersal longline fisheries. By combining these two approaches, our primary aim was to investigate the occurrence of odontocetes depredation events on demersal longlines in the underwater dimension during both soaking and hauling phases of the fishing process. The initial objective was to use the two methods to study both killer whales and sperm whales.

Unfortunately, this turns out to be impossible. In the following, we will see that accelerometers deployed on longlines have been useful to study sperm whales (Sec. "longline accelerometry"), while tags deployed on animals were useful to study killer whales only (Sec. "Odontocetes tracking data loggers").

METHODS

RESEARCH CONTEXT

The study focused on a depredation conflict involving the French Patagonian toothfish fishery with killer whales and sperm whales. The Patagonian toothfish is a long-lived (>50 years) and a large (> 200 cm in length and > 200 kg in weight) species [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF],

with high-quality flesh making the species economically highly valuable [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Grilly | The price of fish: A global trade analysis of Patagonian (Dissostichus eleginoides) and Antarctic toothfish (Dissostichus mawsoni)☆[END_REF]. The French longline fishery is of particular scientific interest since it holds the largest Patagonian toothfish quota in the Southern Ocean (COLTO 2016) allocated between the subantarctic islands of the Crozet Archipelago (46°25′S, 51°59′E) and Kerguelen Islands (49°20′S, 70°20′E, see Figure 456). Additionally, this fishery also experiences the highest depredation levels of all toothfish fisheries, with more than 30% and 9% of the total annual catch taken at Crozet and Kerguelen, respectively, by killer and sperm whales [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF][START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. Interaction levels are fundamentally different between the two EEZs with sperm whales being present around both islands but at different densities [START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF]) and killer whales being almost exclusively found at Crozet [START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF].

Fishing seasons last a year, starting in September and ending in August. A fishing season consists of 3 or 4 trips of approximately 3 months each. During a trip, vessels fish continuously through a diel pattern. Longlines are set at night and primarily hauled during the day, since fishing regulations prohibit setting at daylight to avoid seabird bycatch [START_REF] Weimerskirch | Factors affecting the number and mortality of seabirds attending trawlers and long-liners in the Kerguelen area[END_REF]. During trips, all longline positions (latitude and longitude), bathymetry at deployments (500 -2000 m), and setting and hauling times are recorded. Fishing in waters shallower than 500 m is prohibited to avoid the capture of juvenile toothfish [START_REF] Collins | Chapter Four -The Patagonian Toothfish: Biology, Ecology and Fishery[END_REF][START_REF] Gasco | Contributions to marine science by fishery observers in the French EEZ of Kerguelen[END_REF].

Vessels use auto-weighted longlines set between two anchors and linked to buoys at the surface for retrieval. The lines are composed of sections of 750 hooks, with 1.2 m between hooks. The length of the longlines varies from 1 to 40 km, with an average of approximately 8 km. For each longline hauled, the presence of cetaceans (killer whales and/or sperm whales) is monitored according three classes: (i) whales absent (condition suitable for a confident observation); (ii) whales present; and (iii) uncertain-observation (conditions unsuitable and/or no observation undertaken). Data were available through the PECHEKER database (Museum National d'Histoire Naturelle de Paris; [START_REF] Martin | Pecheker, relational database for analysis and management of fisheries and related biological data from the French Southern Ocean fisheries monitoring scientific programs[END_REF].

Data collection for this study was conducted from two commercial demersal longline fishing vessels during two summer trips (December -March) in 2016-2017 and in 2017-2018. ≤250 m (Collecte Localisation Satellites, Toulouse, France). These accuracies were used to determine a confidence area around the ARGOS positions. Position without uncertainty estimates (classes i) were removed from the dataset. ARGOS positions were processed using the software R (version 3.4.4, R Development Core Team). We mapped every position using a buffer function (create.buffer, package marmap version 1.0.2) to account for potential location accuracy errors. Bathymetric data were obtained from the ETOPO1 dataset (NOAA) and plotted using a custom R code. For a given ARGOS position and associated date/time, all longlines at sea and their status (soaking, hauling) were also plotted on the map. We then estimated the distance from the instrumented animal to the closest longline using the ARGOS positions and the longline coordinates.

From there, we defined interactions between an individual and the fishing gear based on the geographic proximities of both entities. We used a method that defines odontocete interactions with fishing vessels at hauling in other studies [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF][START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF]. Animals were considered to be interacting with a longline if they were within a 1.5 km proximity, independent of the fishing activity. Thus, we determined two behavioral states for every individual's ARGOS position: (i) 'interaction' with a longline; and

(ii) 'no interaction' with any longline. Simultaneously, we monitored the individual's depth profile of every ARGOS position. We also added the depths of the closest longlines to the diving profile when the individual was in 'interaction'. Moreover, if the closest longline was being hauled, we also monitored bathymetry under the boat during the hauling. As the equipped individual was in interaction with the boat at this time, we considered it to be in waters with the same bathymetry than the fishing vessel.

All instrument deployments followed the ethics policies of the Terres Australes et Antarctiques Françaises (TAAF) and were authorized by the Réserve Naturelle Nationale (RNN des TAAF) through approval A-2017-154.

LONGLINE ACCELEROMETRY

We deployed 3-axis accelerometer/pressure sensor data loggers (Sextant Technology, New Zealand) on longlines to detect activity events at the hooks (fish catch and/or depredation events) and the depth at which they occurred during soaking and hauling. Accelerometer/depth data loggers were deployed singularly on snoods (i.e. short lines connecting individual hooks to the main longline; Supp. data 8). In the 2016/2017 field season, the data loggers were attached to snoods by a snap connector fixed on the mainline (Supp. data 8) while in 2017/2018 they were attached with a thick rope to the swivel between the snood and the main line (Supp. data 8), allowing the data loggers to roll around the mainline as normal snoods do. We used two different versions of data logger: 2016/2017 recorded acceleration at 10 Hz with a precision of 10 bits; 2017/2018 recorded acceleration at 12.5 Hz with a precision of 12 bits. In both versions, the acceleration range was set at ±16 g per axis and, to conserve battery life and memory capacity, an acceleration threshold (2016/2017 0.03 g, 2017/2018 0.01 g) was set to start recording when a movement occurred on the hook. The pressure sensor recorded continuously at 0.2 Hz.

Sets of accelerometers were deployed along a longline on every hook (i.e. every 1.2 m), or separated by 3, 5 or 10 unequipped hooks. When the equipped longline was retrieved on the vessel, the presence of a captured fish (and its species) on an equipped snood was recorded. For equipped hooks without any capture, we recorded whether the bait was still present and the condition of hook (undamaged, twisted or ripped off the snood).

To assess the potential for detecting events on non-equipped snoods, the distance from equipped hooks to nearest capture along the longline was recorded, counting 0 when a catch occurred on the equipped hook. Simultaneously, we monitored the amplitude of the movement received on the loggers nearby. Thus, we estimated the norm of the acceleration vector:

EQUATION 4-3 Norm= √𝒂𝒙² + 𝒂𝒚² + 𝒂𝒛²
with ax, ay and az the 3 components of the acceleration vector. Acceleration data were extracted using the software Hermes DeepG (Sextant Industry, New Zeland). Accelerometer data and pressure profiles were processed using custom-built routines in Matlab (version R2015, The MathWorks, Natick, MA, USA).

BIO-LOGGING 123

We then examined how the acceleration norm (Equation 4-3) varied with respect to the distance (in number of hooks) of the closest capture. To do so, we produced boxplots depicting the median with the 25th and 75th percentiles [START_REF] Mcgill | Variations of Box Plots[END_REF]. Also a 95% of confidence interval was represented (the two whiskers) with the outlying points shown individually [START_REF] Mcgill | Variations of Box Plots[END_REF]). The dataset did not allow for linear regressions to be assessed because of a violation of independence when a same catch was monitored on several accelerometers nearby. The low number of accelerometers recording signals from a same catch did not allow for nested linear models to be used to correct the violation of independence. Boxplots were developed per season because of the difference in the sampling schedule of the loggers and their method of attachment to the longlines between the two field seasons Finally, we assessed the depth profile of each accelerometer and mean norm acceleration. We manually looked for any depth anomalies and assessed the distance of closest capture to equipped hooks. Our aim was to determine if the acceleration/depth data could reveal depredation events for hooks that were hauled without fish. The dive data corresponding to the 9 'interaction' positions on longlines being hauled indicated that killer whales started diving at the beginning of hauling activities and stopped diving once all hooks were landed onboard the vessel (Figure 4-7 and Supp. Data 10 to 14).

Interestingly, during the hauling operations, the diving depths of the killer whales ranged throughout the entire water column (from the surface to the sea-floor).

The dive data corresponding to the 14 "interaction" positions on soaking longlines indicated that the killer whales mostly performed shallow dives (<50 m). An exception to this behavior occurred for one "interaction" position, where a killer whale performed a dive to 502 ± 22 m only 1:30 h after the "interaction" position time . This "interaction" position was the last of a series of 6 consecutive positions recorded within a 4.5 h window and all overlapping with the same cluster of longlines soaking within a 6 km radius (Figure 4 We observed with the two loggers that 68% of the ARGOS positions with 'no interaction' were associated with shallow dives (< 50 m) and occurred between two positions with 'interaction'. However, for one of the two instrumented individuals, 9 "no interaction" positions coincided with 7 relatively deep dives. The maximum depths were between 200 and 325 m, and 3 of these dives were performed within a 3 h period (Figure 4-9 and Supp. data 15).

This specific event occurred on a seamount. The area is characterized by steep slopes reaching a plateau at depths of approximately 200-300 m, with two peaks rising to depths of up to 100 m. As fishermen are not allowed to set longlines on the seafloor shallower than 500 m, these deep dives could not be associated to longline depredation. ). The catch rate of Patagonian toothfish on the equipped hooks (3.42%) was similar to the catch rate of all longlines for the whole fleet for the same period (3.85%).

However, due to device malfunctions, accelerometer data were obtained for only 13 toothfish captures.

Acceleration norms recorded during the second season were higher than those recorded during the first season (Figure 4-10). This is likely due to modifications in the newer generation of accelerometers that were used on the second year of the study, and to modifications in the way accelerometers were attached to longlines. In the second year, a smoother attachment was used, allowing a complete rotation of the snood around the mainline. . However, for both field seasons, the accelerometer data showed the same feature: the accelerometer norm globally decreases when the distance of the closest capture increases (Figure 4-10). Over the total 126 sections of data logger deployments, we observed 3 events of significant depth variation during three separate longline soaking phases, one at Kerguelen in January 2017, one at Crozet in February 2017 and one at Crozet in February 2018 .

These events occurred at depths of 600, 1600 and 1800 m, respectively, while the lines were soaking on the seafloor. The elevation events lasted 6, 9 and 52 minutes, respectively (Figure ). However, a sperm whale was found entangled and dead (Fig. S2) on the longline of the third event (Figure 4-11.E.F.). The logger was located 1km from the dead sperm whale. During the three events, all equipped hooks were hauled without caught fish and one of the equipped hooks was hauled in a row of ten hooks twisted or ripped off the snood. The accelerometer of the third elevation event (i.e. with the dead sperm whale) did not reveal any acceleration activity while the longline was on the sea floor. This suggests that no fish were captured on any of the hooks located near the logger. However, the loggers monitoring the two other events revealed acceleration occurring before each elevation event and then stopped recording any activity until the hauling process began. This indicates the occurrence of a fish capture and then depredation. We then compared the mean acceleration norm of the equipped hook during the soaking phase until these elevation events with the boxplot of mean acceleration norm per closest capture . These comparisons allowed for the distances of the activities recorded on the accelerometers to be roughly estimated before the elevation events occurred. We observed that the mean acceleration norm before the second elevation event (Crozet 1617) was higher than the lower quartile of the boxplot at a distance of 0, i.e. fish hauled on the equipped hook . This revealed that the equipped hook, hauled undamaged and with no fish, probably caught a fish during the soaking and before the elevation event. In contrast, the the mean acceleration norm before the elevation first event The diving behavior of instrumented killer whales showed that individuals actively and repeatedly dived to depths matching those of longlines while they were being hauled onto vessels. Interestingly, these dives were performed as soon as hauling was initiated by fishermen, with the first dives being the deepest (on occasions >600 m) and matching the seafloor depth.

Together, these findings suggest that depredating killer whales readily expend high amounts of energy in deep, short-spaced successive dives and that these costs are likely outweighed by the benefits gained from feeding on toothfish caught on hooks. These benefits may be maximized if individuals are the first to access the resource offered on the hooks, potentially with a choice of bigger fish. It may therefore be hypothesized that deep dives performed at the beginning of hauling is a response to both intra-and inter-specific competition. Competition is likely generated by a highly localized, short-term availability of easy-to-catch resource, such as toothfish caught on longlines. The large concentrations of both killer whales and sperm whales (sometimes co-occurring) around fishing vessels suggests such competition [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Tixier | Interactions of Patagonian toothfish fisheries with killer and sperm whales in the Crozet islands Exclusive Economic Zone: an assessment of depredation levels and insights on possible mitigation strategies[END_REF]. Deep diving behavior while depredating on longlines being hauled has also been reported for killer whales off South Georgia [START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF]) and for sperm whales in Alaska [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF], suggesting competition for the hauled resource in both cases.

In the present study, the diving/tracking data for the killer whales and the longline accelerometry/depth data for the sperm whales suggest that these species also interact with longlines during soaking. For killer whales, interactions with longline sets on the seafloor during soaking phases are suggested by the matched maximum dive depths and bathymetry when positions of individuals overlapped with those of longlines. Additionally, the repeated deep dives within a short duration (5 dives in 2 hours) to the same depth, strongly suggests a foraging activity on a highly localized resource remaining available at the same depth for extended periods of time, strengthening the idea that the killer whale was foraging on the soaking longlines. While more data are required to fully address these issues, our dataset demonstrates that killer whales can forage on soaking longlines and suggests that they do.

The unfortunate by-catch of a sperm whale entangled in a longline equipped with a logger confirms the species does depredate on soaking longline. The event also helps the interpretation of the other longline logger data. The elevation signals detected on loggers were identified as interaction events and confirmed by additional cues such as toothfish capture events on the same portion of sets, wrested and twisted hooks, and the presence of sperm whales in the vicinity of sets. While such cues were undetected for one of the nominally identified events, the depth of the event (1600 m) makes it unlikely to be the result of killer whales as they are not known to dive deeper than 1100 m [START_REF] Reisinger | Movement and diving of killer whales (Orcinus orca) at a Southern Ocean archipelago[END_REF][START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF]. In contrast, sperm whales are known to be able to reach depths of 1500-2000m [START_REF] Teloni | Shallow food for deep divers: Dynamic foraging behavior of male sperm whales in a high latitude habitat[END_REF][START_REF] Fais | Sperm whale echolocation behaviour reveals a directed, prior-based search strategy informed by prey distribution[END_REF][START_REF] Guerra | Diverse foraging strategies by a marine top predator: Sperm whales exploit pelagic and demersal habitats in the Kaikōura submarine canyon[END_REF]).

In addition, the variation in depth data obtained during longline soaking suggests how depredation events may occur. The two elevations of longlines up to 30 and 40 m off the seafloor indicate a significant pull must have been exerted directly on the line, and not on a hooked toothfish. Pulling on a hook or a fish may only support an elevation of 1 or 2 m, as observed in video data obtained by [START_REF] Van Den Hoff | Southern elephant seals (Mirounga leonina Linn.) depredate toothfish longlines in the midnight zone[END_REF] showing an elephant seal pulling a toothfish to unhook it. Furthermore, sperm whales depredating hauled lines near Alaska appear to bite and scrape sections of lines in order to remove fish instead of directly targeting hooked fish [START_REF] Mathias | Relationship between sperm whale (Physeter macrocephalus) click structure and size derived from videocamera images of a depredating whale (sperm whale prey acquisition)[END_REF][START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF]). In the present study, the observation of twisted and wrested hooks in a row, even if no fish captures were recorded in the accelerometry record, suggests that sperm whales rake the mainline while lifting it from the seafloor. Such a hypothesis may also explain why the dead sperm whale hauled on a longline with equipped hooks had the mainline wrapped around its jaw. It is also known from subsurface video data that killer whales are more likely to pull fish to remove them from lines [START_REF] Guinet | Long-term studies of Crozet Island killer whales are fundamental to understanding the economic and demographic consequences of their depredation behaviour on the Patagonian toothfish fishery[END_REF] such that it is unlikely this species was involved in elevation events of soaking longlines.

FISHERIES MANAGEMENT AND ODONTOCETE CONSERVATION

IMPLICATIONS

This study has major implications for the way depredation is estimated and incorporated into fish stock assessment as well for the conservation of depredating odontocete populations.

Our results demonstrate that visual observations from fishing vessels are not enough to correctly quantify depredation rates. Indeed, depredation rates are estimated by the difference between catch per unit effort on longlines in absence of cetacean and longlines in presence of cetaceans (e.g. [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Purves | Killer whale (Orcinus orca) and sperm whale (Physeter macrocephalus) interactions with longline vessels in the Patagonian toothfish fishery at South Georgia, South Atlantic[END_REF][START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF], Gasco et al. 2015). Within cases that seafloor depredation occurs on longlines hauled in the absence of cetaceans, depredation rates will be underestimated. This insight has significant implications for fish stock management, since even with the recent efforts to consider depredation in quota management [START_REF] Roche | Marine mammals and demersal longline fishery interactions in Crozet and Kerguelen Exclusive Economic Zones: an assessment of depredation levels[END_REF][START_REF] Gasco | Comparison of two methods to assess fish losses due to depredation by killer whales and sperm whales on demersal longlines[END_REF], our study shows that the fishing stock might be more impacted than previously assumed. Furthermore, to clearly estimate the impact of depredation on the fish stock it is essential to know whether the targeted fish belong to the natural diet of the depredating odontocetes.

While Patagonian toothfish has previously been described as a natural prey of sperm whales [START_REF] Yukhov | Antarkticheskij Klyklash[END_REF], doubts remain about killer whales naturally feeding upon this prey. In the present study, we observed killer whales diving to the seafloor of a seamount, where no longlines were set. This suggests that killer whales naturally forage in this area, though it is not known on what prey. Killer whales at Marion Island have been similarly observed foraging on the seafloor of a seamount at 800 m depth, where they were considered as preying upon squids or Patagonian toothfish [START_REF] Reisinger | Movement and diving of killer whales (Orcinus orca) at a Southern Ocean archipelago[END_REF]. Thus, we also assumed that Crozet killer whales may naturally forage either on squids or on Patagonian toothfish. This hypothesis was already suggested since this population immediately learned to depredate toothfish in 1996 (Guinet et al. 2015). Under this assumption, depredation may therefore have a limited impact on the toothfish stocks but it nonetheless suggests that fishermen and odontocetes are clearly in competition for the same resource.

The dead sperm whale found entangled in the gear and reported here highlights the potential risk of bycatch. This incident is the fifth of its kind reported at Crozet between 2007 and 2018, which represents a bycatch rate of 0.04 % individual per longline over that period.

Among these 5 bycatch events, three resulted in the death of a sperm whale, which represents 2.6 % of the 114 known individuals of the Crozet population [START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF]. While this proportion is low, it still may significantly impacts this low fecundity, long-lived marine mammal [START_REF] Whitehead | Sperm Whale[END_REF]). In addition, the increase of sperm whale bycatch rate in recent years (4 of the 5 reported events occurred in the past 3 years) raises concern about a potentially increased competition between the fishing activity and the local whale populations, which may be due to a greater dependency to depredation and/or a depletion of the toothfish stock.

Although the easy-to-get food provided by longlines may complement an individual's energy intake and improve reproduction (Tixier et al. 2015b;[START_REF] Tixier | Demographic consequences of fisheries interaction within a killer whale (Orcinus orca) population[END_REF], seafloor depredation may pose serious threats to odontocete populations by an increase in entanglement risk.

Previous efforts to minimize odontocetes depredation on demersal longline fisheries have primarily relied on the assumption that fish were removed from hooks only during hauling of longlines [START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. However, if both killer and sperm whales depredate fish on the seafloor as suggested by the present study, efforts to develop new mitigation techniques should be re-orientated to the development of deterrence/protection systems of the longline/hooks throughout the whole soaking and hauling periods. Until now, solutions have mostly been targeted at hauling operations where it might be easier to apply systems to protect the caught fish, such as acoustic deterrent devices to switch on while hauling longline, e.g. the 'OrcaSaver' system (Tixier et al. 2015a), or floating net sleeves sliding down over individual caught fish when the longline is hauled to protect it from depredating whales, e.g. the 'Cachalotera' [START_REF] Moreno | Significant conservation benefits obtained from the use of a new fishing gear in the Chilean Patagonian toothfish fishery[END_REF]). However, these mitigations solutions may be costly and difficult to implement if they require changing fishing gear, or they may be efficient only for a while before odontocetes understand how to bypass these devices (Tixier et al. 2015a).

Rather, our results suggest changing the fishing system with a global protection of the targeted fish, such as fishing pots, may be needed. However, new fishing methods may not be as efficient as the conventional fishery. For instance, in Alaska pot fisheries have been approved and seem to be effective in preventing sperm whale depredation but they are more expensive when compared to conventional longlines [START_REF] Sullivan | North Pacific Fishery Management Council[END_REF][START_REF] Peterson | Sablefish mortality associated with whale depredation in Alaska[END_REF]. The same conclusion has been drawn after a preliminary trial performed as part of the ORCASAV program in 2010 around Crozet Archipelago (captains communications, personal observations and see [START_REF] Bavouzet | Rapport de la campagne casier Orcasav : Une campagne innovante de pêche aux casiers dans la ZEE de Crozet pour lutter contre la déprédation des orques et la mortalité aviaire[END_REF][START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF].

Further investigations should examine whether the occurrence of sea-floor depredation is negligible compared to depredation during hauling. Such quantification would allow for the extent to which depredation rates are underestimated to be assessed and this information would help in determining whether efforts should be put to develop mitigation devices that protect the hooks during hauling only or during the whole fishing process to reduce the economic losses caused by depredation. This study provided preliminary insights to this aspect by suggesting that seafloor depredation might occur more sporadically for killer whales than for sperm whales.

With three interaction events recorded for sperm whales over a low coverage of the fishing effort by accelerometers (~0.02% of hooks set by fishermen), we might assume that depredation on longlines on the seafloor during soaking may be relatively frequent for that species.

Increasing the bio-logging effort on individuals with longer logger deployment might bring more cues on the occurrence of this behaviour. Alternatively, the use of passive acoustic monitoring may help quantifying depredation at seafloor, since killer whales and sperm whales are vocal animals and use echolocation to forage [START_REF] Norris | The echolocation of marine mammals[END_REF][START_REF] Barrett-Lennard | The mixed blessing of echolocation: differences in sonar use by fish-eating and mammal-eating killer whales[END_REF][START_REF] Madsen | Male sperm whale ( Physeter macrocephalus ) acoustics in a high-latitude habitat: implications for echolocation and communication[END_REF][START_REF] Miller | Sperm whale behaviour indicates the use of echolocation click buzzes "creaks" in prey capture[END_REF][START_REF] Watwood | Deep-diving foraging behaviour of sperm whales (Physeter macrocephalus)[END_REF][START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF]. Thus, the clicks can be used as an acoustic proxy of the depredation behaviour, which can help to assess the depredation rates during interactions between soaking and hauling [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF]).

CONCLUSION

Using bio-logging technology on both odontocetes and demersal longlines, this study brought new behavioral insights into sperm whale and killer whale depredation behavior on demersal longlines. Depredation was confirmed during hauling phases from the observations of killer whales diving behavior around the fishing gear in the water column during that phase as described at South Georgia [START_REF] Towers | Movements and dive behaviour of a toothfish-depredating killer and sperm whale[END_REF]. More importantly, although the capabilities of sperm whales to interact with the longline on the seafloor has been previously suggested [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]) our results confirm that sperm whales do, and that killer whales very likely also, depredate on longlines while they are soaking on the seafloor. Although seafloor depredation still needs to be accurately quantified, we have demonstrated the occurrence of this behavior which has major implications both for past depredation assessment and management, and for future mitigation developments.

ACKNOWLEDGMENTS

We are particularly grateful to the captains and their crew of the Mascareignes 3, the Ile de La Réunion and the Albius for their assistance in the field. We thank Dominique Filipi for providing the accelerometer and for his help with the devices. We are also grateful to fishery

CONCLUSION

Biologging techniques are useful tools to assess the depredation conflict because these techniques allow for a monitoring of interactions at the "longlines-odontocetes" scale, which is finer than the "fishery-odontocetes" scale. Deployments on longlines have brought a new lead for a "fishing gears behaviour" monitoring, especially while soaking. Within this thesis we provided evidence of seafloor depredation on soaking demersal longlines by odontocetes (section 4.3), but the extent to which this behaviour occurs remains unknown. Nevertheless, with 3 interaction events recorded for sperm whales over a low coverage of the fishing effort by accelerometers (~0.02% of hooks set by fishermen have been equipped), we might assume that depredation on longlines on the seafloor during soaking may be relatively frequent.

Therefore, an acoustic approach would be interesting to assess interaction during soaking time as killer whales and sperm whales are vocal animal and used echolocation to forage [START_REF] Norris | The echolocation of marine mammals[END_REF][START_REF] Barrett-Lennard | The mixed blessing of echolocation: differences in sonar use by fish-eating and mammal-eating killer whales[END_REF][START_REF] Madsen | Male sperm whale ( Physeter macrocephalus ) acoustics in a high-latitude habitat: implications for echolocation and communication[END_REF][START_REF] Miller | Sperm whale behaviour indicates the use of echolocation click buzzes "creaks" in prey capture[END_REF][START_REF] Watwood | Deep-diving foraging behaviour of sperm whales (Physeter macrocephalus)[END_REF][START_REF] Zimmer | Passive Acoustic Monitoring of Cetaceans[END_REF]). Thus, the combination of hydrophones (section 3) and accelerometers (section 2) on longline would enable to have a better covering of interaction when fishing boats are not around the longlines and to estimate the soaking depredation rates.

Despite the small number of individuals equipped, the short duration of loggers deployed on killer whales and low data resolution, results from this study demonstrate that animal-borne biologging techniques are determinant in understanding the underwater dimension of the depredation conflict. Additional deployments of loggers with longer lifespan and better resolution, on both killer and sperm whales, would help further characterisation of the depredation behaviour of these species underwater. For instance, with a higher spatiotemporal resolution of tracking data, we should be able to characterise the time and the distance at which odontocetes detect and make the decision to head towards fishing activities [START_REF] Collet | Albatrosses redirect flight towards vessels at the limit of their visual range[END_REF]. By recording tracking data over extended periods of time, bio-logging may provide insights on where these odontocetes naturally forage in absence of vessels. For killer whales, although they can be seasonally observed from the Crozet islands while foraging on penguins or pinnipeds [START_REF] Guinet | Intentional stranding apprenticeship and social play in killer whales ( Orcinus orca )[END_REF][START_REF] Guinet | Development of intentional stranding hunting techniques in killer whale (Orcinus orca) calves at Crozet Archipelago[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF], Guinet et al. 2015) they may feed on other prey items which are yet to be determined. As for sperm whales it would allow to confirm their fidelity to some foraging grounds as suggested from photo-identification [START_REF] Labadie | Sperm whales of the Crozet and Kerguelen islands -Photo-identification catalogue[END_REF][START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF] and to assess whether they are preferentially distributed on spots of high toothfish density. As sperm whale is a migratory species, long duration tracking would help to identify breeding sites, which remains unknown to date. Combined to satellite tracking, depth recorders are essential to assess the use of the water column by these predators, either while depredating or while foraging naturally. Similarly to killer whales around Crozet (section 4.3), it would be important to describe whether sperm whales could perform depredation both near the surface and close to the bottom, such as observed on sperm whales depredating on halibut fisheries in Alaska [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF]. Loggers on sperm whales could also add some complementary description to the sea-floor depredation and may quantify the recurrence of this behaviour for an equipped individual. Although we have been able to observe some natural diving behaviour for an equipped killer whale, the amount of data available during times when there was no fishing activity was not sufficient to investigate natural foraging behaviours. We also did not dispose of a bathymetric dataset precise enough to assess whether the whales forage on the seafloor. This aspect is also of great interest since dives in different layers strongly suggest that individuals forage on different prey types. Further investigation would also require deploying new generations of loggers processing on-board acceleration on both species (section 4.2). Information on prey catch attempts is crucial to accurately determine where the odontocetes focus their foraging strategies and would allow comparing foraging efficiency between natural and depredating behaviours. Estimation of prey catch attempts would enable to quantify the number of fish remove at an individual scale, similarly to the use of buzzes of echolocation in Alaska to quantify depredation rates [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF].Investigation of the natural foraging behaviour may also highlight how these species found their food and on which prey they naturally forage. In a further extent, knowing whether these predators forage on Patagonian toothfish naturally and how much this prey represent in their diet proportion would allow a quantification of the impact of depredation on fish stocks. On-board accelerometers could also transmit some proxies of swimming effort [START_REF] Cox | Processing of acceleration and dive data on-board satellite relay tags to investigate diving and foraging behaviour in free-ranging marine predators[END_REF]. As observed with southern elephant seals, such proxies could directly be correlated with body condition [START_REF] Richard | Variation in body condition during the post-moult foraging trip of southern elephant seals and its consequences on diving behaviour[END_REF][START_REF] Richard | Southern Elephant Seals Replenish Their Lipid Reserves at Different Rates According to Foraging Habitat[END_REF]. Although depredation has been described to have benefits on killer whales reproduction (Tixier et al. 2015a), such loggers may quantify differences of foraging costs at an individual scale between natural and depredation behaviours. Altogether, these investigations may bring then crucial information on the influence of depredation on odontocetes behaviours.

DISCUSSION

A clear identification of the actors involved in a conflict is necessary to achieve the correct management [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Young | The emergence of biodiversity conflicts from biodiversity impacts: characteristics and management strategies[END_REF][START_REF] Colyvan | The conservation game[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sullivan | North Pacific Fishery Management Council[END_REF]. Within fisheries depredation conflicts, the actors are mostly described between the "depredating species" and the "depredated fishery". The "fishery" gathers "fishermen" and their "fishing gear" as the interaction is commonly observed when fishermen recover their fishing gear. Although depredation is known to occur during soaking time on pelagic longlines (Dalla [START_REF] Rosa | Killer whale (Orcinus orca) interactions with the tuna and swordfish longline fishery off southern and south-eastern Brazil: a comparison with shark interactions[END_REF][START_REF] Forney | What's the catch? Patterns of cetacean bycatch and depredation in Hawaii-based pelagic longline fisheries: Patterns of cetacean bycatch and depredation in Hawaii[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF][START_REF] Thode | Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation[END_REF], the distinction between longlines and fishermen has never been clearly expressed.

Indeed, "interactions" for demersal longline fisheries have been assumed to primarily occur during hauling phases when animals are visually observed from the vessels [START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF][START_REF] Tixier | Déprédation par les orques (Orcinus Orca) et les cachalots (Physeter Macrocephalus) sur les palangriers à la legine australe dans la ZEE de l'archipel de Crozet[END_REF][START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. The underwater dimension of the conflict has then never been clearly investigated. Thus, questions such as when, where and how odontocetes interact with demersal longlines remain unclear. These gaps of knowledge have raised uncertainties both on the estimation of depredation rates and on the way to find suitable mitigation measures.

The purpose of this thesis was to bring new insights on the depredation conflict with a finer definition of the actors involved in the "fishery-odontocetes" interaction. In this thesis, the distinction was made for the interactions between the three actors: fishermen, fishing gears and odontocetes. This categorisation of entities allowed assessing individual behavioural ecology for both fishermen and odontocetes, which has also been sparsely explored. From this approach I aimed at bringing new insights on these competition/ interactions between these actors. In this discussion I will outline in which extent this thesis answered some of these questions using the study case of the Patagonian toothfish longline fishery around French Sub Antarctic islands.

Although some gaps are filled, new ones open up. As a result this discussion ends with new perspectives on the depredation issue.

HOW TO EXPLAIN INTER-CAPTAINS VARIABILITIES OF INTERACTION RATES?

Within the purpose to monitor the perceptions of conflicts as well as the behaviours of the involved actors [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Blackwell | No single solution: application of behavioural principles in mitigating human-wildlife conflict[END_REF], the use of human behavioural ecology [START_REF] Nettle | Human behavioral ecology: current research and future prospects[END_REF]) can bring new highlights on the relationships between "fishermen-odontocetes" and "fishermen-fishing gear". This approach aims at assessing whether fishermen behaviours may influence the probability of interaction with predators based upon the optimal foraging theory [START_REF] Mccay | Systems ecology, people ecology, and the anthropology of fishing communities[END_REF][START_REF] Pyke | Optimal Foraging Theory: A Critical Review[END_REF][START_REF] Nettle | Human behavioral ecology: current research and future prospects[END_REF].

As other foragers, fishermen behaviour could be assessed through their exploration and exploitation of resources, or through finer scale with their navigation behaviour, i.e. the use of their fishing vessels, to avoid detectability. As captains are the decision-makers, variations of their behaviour may explain part of the between-captains observed variability in interaction rates with odontocetes highlighted on Figure 2-1. Firstly, I showed in Chapter 2 that although all captains behave like optimal foragers, they have different fulfilment of a fishing success and they perceive the competition differently. As such, a captain being less sensitive to depredation, i.e. with a lower probability to leave a patch or with a higher probability to haul the following closest longlines in presence of odontocetes, would consequently show higher interaction rates.

Secondly, as I introduced in Chapter 3, captains frequently manoeuvring vessels and decelerating roughly may increase their acoustic detectability by odontocetes, suggesting then an increase of their interaction rate. A change in this behaviour, i.e. hiding the fishing activity, would be akin to stealth behaviour or camouflage.

Altogether, these results suggest that the behaviour of captains is likely to be an important driver of their probability to interact with odontocetes. More broadly, these results emphasize the importance of human behavioural ecology approaches in studies addressing human-wildlife conflicts [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Blackwell | No single solution: application of behavioural principles in mitigating human-wildlife conflict[END_REF]). These new insights would be useful to further optimize fishermen behaviour to reduce depredation as they could be combined to other aspects of captains' behaviour (e.g. operational variables) to develop effective and easy-to-implement mitigation strategies. Besides, since the optimal foraging theory is an ecological method derived from economy fields, I suggest using such human behavioural ecology approach to assess the socio-economic impacts of the conflict.

Quantifying the economic impact of depredation on fisheries and modelling the various costs generated by this conflict would allow determining better ways to optimise fishing success while minimising the risk of interaction with odontocetes. Further studies on the fishing success optimisation should also include the level of experience of captains. As an analogy to a juvenile forager naïve towards its environment, we may consider that captains fishing for the first time have to learn how to find fish stocks and how to face depredation. However, most of captains when commanding during their first trip have already some experience as chief officer and have learned for former captains. As such, increased experience and apprenticeship with experienced captains is expected to result in changes in the fishing strategy to minimize depredation and optimise fishing success. The variables composing this improved strategy should be therefore identified. In addition, increased experience may also improve the captains' manoeuvrability of their vessel. Differences of detectability according to different way captains use the vessel's acoustics should also receive further attention to confirm such hypothesis.

While the identification of optimal fishing strategies and stealthier navigation behaviour would be critical for mitigation measures to be developed, odontocetes may potentially learn to overcome these strategies and hence maintain a competitive pressure on captains. As such, systems providing captains with real-time information about the presence of odontocetes would allow them to adjust their fishing strategy accordingly. The example of odontocetes acoustics exposed in Chapter 3, revealed the interest of PAM to provide presence information around the buoy prior to hauling. This measures would require to implement an automatic detector (e.g.

Deteclic, see summary in Appendix 2) with a real-time processing on hydrophones (e.g.

PAMGUARD cf [START_REF] Gillespie | PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localization of cetaceans[END_REF], in the Soundtraps) and to transmit predators' presence information around the longline via VHF, satellite or mobile phone signals [START_REF] Van Parijs | Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales[END_REF]. Another approach would consist to use a towed array from the fishing vessel to detect the presence of odontocete [START_REF] Thode | Tracking sperm whale (Physeter macrocephalus) dive profiles using a towed passive acoustic array[END_REF][START_REF] Weir | Comparative Review of the Regional Marine Mammal Mitigation Guidelines Implemented During Industrial Seismic Surveys, and Guidance Towards a Worldwide Standard[END_REF][START_REF] Van Parijs | Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales[END_REF][START_REF] Yack | Passive acoustic monitoring using a towed hydrophone array results in identification of a previously unknown beaked whale habitat[END_REF], before starting to set new longlines. Whatever the chosen technology solution, a real-time PAM should give captains one step ahead of their competitors. Inspired by the work conducted in this thesis, a fishing company has bought a towed hydrophone and will make the first trials during the fishing season 2018-2019.

WHEN, WHERE AND HOW DO INTERACTIONS OCCUR?

Until now, depredation on demersal longlines was assumed to only occur during the hauling (Hucke- [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Gilman | A review of cetacean interactions with longline gear[END_REF][START_REF] Read | The Looming Crisis: Interactions between Marine Mammals and Fisheries[END_REF][START_REF] Mathias | Acoustic and diving behavior of sperm whales (Physeter macrocephalus) during natural and depredation foraging in the Gulf of Alaska[END_REF][START_REF] Rabearisoa | Pelagic longline fishing trials to shape a mitigation device of the depredation by toothed whales[END_REF][START_REF] Söffker | The Impact of Predation by Marine Mammals on Patagonian Toothfish Longline Fisheries[END_REF], Guinet et al. 2015[START_REF] Passadore | Depredation by killer whale (Orcinus orca) and false killer whale (Pseudorca crassidens) on the catch of the Uruguayan pelagic longline fishery in Southwestern Atlantic Ocean[END_REF], Tixier et al. 2015c[START_REF] Werner | Mitigating bycatch and depredation of marine mammals in longline fisheries[END_REF]. Additionally, how odontocetes detect longliners and fishing activity has only been investigated on sperm whales in Alaska and with focus limited on acoustic cues produced during hauling activity [START_REF] Thode | Observations of potential acoustic cues that attract sperm whales to longline fishing in the Gulf of Alaska[END_REF][START_REF] Thode | Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation[END_REF]. In Chapter 4, I showed that both killer whales and sperm whales depredate on longline while soaking on the seafloor.

This brings new insights on the capacity of odontocetes to interact with longlines at depth. The results also suggest that sperm whales take the longline into their mouth, rake it to unhook and then catch toothfish as depicted in Alaska (Mathias et al. 2013). However, the 'deep' depredation behaviour occurring on hauling longlines described by Mathias et al. (2013) might also depict some seafloor depredation. These findings also indicate that odontocetes can localise fishing activity long before the hauling, and more precisely where the longlines are being deployed at sea. Through the preliminary acoustic analyses in Chapter 3, I observed that odontocetes are likely to know when a longline is being deployed, as vessels produce specific acoustic cues during the setting operation. Further investigation are required to accurately estimate the distance at which these cues propagate, and whether they propagate on a broader range than the cues produced during hauling. For now, I can only speculate that odontocetes may be able to detect the vessel within a 7-35 km range, and then through acoustic signature of the setting operation they may understand when a longline is deployed.

These results strongly suggest that mitigation devices should protect the fish caught on hooks not only during hauling but also during the whole soaking time. As such, a first experimental deployment of fish protective devices was conducted during my second field trip (2017)(2018). The devices were designed by the industrial supplier SATIM to protect individual hooks from odontocetes while allowing the toothfish to enter in the tube and grab the bait (Figure 5-1). Forty six devices were deployed over 36 sections of longlines i.e. 1 to 2 devices per section of 750 hooks. The toothfish had then a choice between easy accessible free hooks and a protected hook harder to access, thus the likelihood of toothfish to enter in the device was assumed to be almost null. A second test was, therefore, conducted with a small section of 15 hooks all equipped with this systems. However, the deployment went wrong and fishermen had to cut the longlines after the 10 th systems dropped off the boat. Because of safety, the experiment was not conducted again. No catch occurred on hooks equipped with the device over 56 deployments. Based on the capture rates obtained with the accelerometers during the same trip (circa 4%), one would assume that 2 toothfish should have been caught over the 56 hooks equipped with the device. A possible explanation of the failure of the test could be that toothfish do not seem to be able to swim backwards (fishery observers and personal observations from fish alive in tagging tank), they are less likely to enter in a tube without possible exit.

Deployments of these devices proved to be challenging and imposed security concerns at sea. The main issue is that the fishing vessels are optimised for one kind of fishing practice.

It is then not possible to modify techniques on-board. One solution could be to change the fishing practice and to switch to fish traps. Although this solution would solve the depredation issue, a preliminary trial performed as part of the ORCASAV program in 2010 revealed that none of the 12 fish trap models tested allowed a fishing success high enough to be economically sustainable (captains and Guinet personal communications, and see [START_REF] Bavouzet | Rapport de la campagne casier Orcasav : Une campagne innovante de pêche aux casiers dans la ZEE de Crozet pour lutter contre la déprédation des orques et la mortalité aviaire[END_REF][START_REF] Gasco | orca), les cachalots (Physeter macrocephalus) et les otaries (Arctocephalus spp.) à Kerguelen et Crozet (Océan indien sud). Conséquences sur la gestion de la pêcherie et évaluation de solutions[END_REF]). Because of these difficulties, further investigation should assess whether the impact of sea-floor depredation is negligible compared to the hauling depredation. Within this assumption, mitigation devices protecting the hooks during the hauling only such as the 'Cachalotera', a floating net sleeves sliding down over individual caught fish when the longline is hauled to protect it from depredating whales (Hucke- [START_REF] Hucke-Gaete | Operational interactions of sperm whales and killer whales with the Patagonian toothfish industrial fishery off southern Chile[END_REF][START_REF] Moreno | Significant conservation benefits obtained from the use of a new fishing gear in the Chilean Patagonian toothfish fishery[END_REF] or the SAGO, a catching pod going down the longline to collect the fish (Arangio 2012), could be enough to reduce the loss in default of deleting depredation. First insights highlighted in the Chapter 4 suggest that seafloor depredation might occur more sporadically for killer whales than sperm whales, but this needs to be accurately quantified. Results obtained from loggers on killer whales, on Figure 4-8, paired with the recording of this species presented on 3-4, suggested that they are more likely to wait for the longline being hauled to start depredating. For sperm whales, the three events observed on the equipped hooks seem noticeable compared to the deployment effort. Besides, depredation behaviour while the line was soaking on the seafloor was suggested in a previous study analysing the PECHKER dataset and described in Appendix 4 [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. In absence of sperm whales at hauling absolute catch per unit effort (CPUE) increases with increasing soaking time. This correlation decreased with an increasing number of sperm whales present during hauling. When more than 5 sperm whales were present, the CPUE decreased with increasing fishing time [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. This change in the relationship was interpreted as an indication that sperm whales may depredate toothfish from longline at the bottom while the line was fishing and the higher the number of sperm whales the greater the number of fish removed during soaking [START_REF] Janc | How do fishing practices influence sperm whale ( Physeter macrocephalus ) depredation on demersal longline fisheries?[END_REF]. The quantification of this depredation on the seafloor would then require deeper investigation.

Within this purpose bio-logging on odontocetes should allow comparing number of depredation events between soaking time and hauling time through an individual perception. Indeed, the bio-loggers deployed on killer whales revealed both depredation behaviours during hauling and during soaking (Chapter 4), thus with an increased dataset it may be possible to compare the number of dives during the hauling as observed on Figure 4-7with the number foraging dives on soaking longlines as on Figure 4-8. Thus, increasing the bio-logging efforts on both species should allow assessing at an individual scale whether the impact of sea-floor depredation is negligible compared to the hauling depredation. Additionally, using clicks as acoustic proxies of depredation behaviour a PAM approach could specify depredation rates during interactions between soaking and hauling [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF].

Considering fishermen and the fishing gear as separate entities, I provided preliminary insights on fishermen detectability and longline depredation on the seafloor but I did not consider how odontocetes may detect the fishing gear. Although future research efforts should focus on vessels detectability and how to make them stealthier, further investigation should also assess detectability of the longlines. Knowing how odontocetes find and see the longline could lead to new countermeasures, such as passive acoustic deterrent to reduce depredation by repelling or by masking the longline. For instance, a deterrent supposed to simulate a swim bladder of a rockfish (Sebastes spp.), species not consumed by sperm whales in Alaska, was tested on longlines targeting halibut [START_REF] O'connell | Testing a passive deterrent on longlines to reduce sperm whale depredation in the Gulf of Alaska[END_REF]. Although the results have not been

very conclusive yet [START_REF] O'connell | Testing a passive deterrent on longlines to reduce sperm whale depredation in the Gulf of Alaska[END_REF], such approach might be replicated on Patagonian toothfish fisheries. Indeed, similarly to Alaska sperm whales also avoid to depredate a species with a swim bladder, the grenadiers (Macrourus spp.), whereas toothfish are deprived of one.

Additionally, other repelling aspect might be tested, such as deterrent simulating a heap of hooks which seems to hinder odontocetes to depredate (fishermen personal communication).

Further investigation could assess the acoustic target strength of longline to bring important insights on how longline could be perceived through odontocetes' echolocation. However, a good understanding of how odontocetes see through their biosonar would be essential to determine whether such mitigation approach is assessable.

ECOLOGICAL IMPLICATIONS

The fishermen optimal foraging behaviour paired with the seafloor depredation behaviour of odontocetes strongly suggest that depredation is underestimated. The amount of fish depredated by odontocetes is currently estimated from the difference of the catch per unit effort, i.e. the quantity of fish caught per hook (CPUE in g.hook -1 ), between longlines in absence of competition and longlines on presence of odontocetes within a fine spatial scale (Gasco et al. 2015). This method is based upon the assumption that the longlines hauled without odontocetes gave a baseline of the natural resource abundance. However, some depredation events may be not accounted for, since whales can interact with the longline soaking on the seafloor when the vessel is away from the gear, and therefore when no visual observation effort is made. Additionally, captains are more likely to keep fishing in presence of whales if the success is high. This type of decision may lead to large numbers of longlines hauled in presence of whales with high CPUEs compared to longlines without competition showing lower fishing success which may generate a bias in depredation estimations.

Further assessment of depredation rates should take into consideration the consequence of captains' decision to stay on a patch in presence of odontocetes. However, such consideration would be difficult to quantify. Conversely using a PAM, as previously described, should allow refining the number of depredation events during the whole fishing process [START_REF] Thode | A comparison of acoustic and visual metrics of sperm whale longline depredation[END_REF].

Within this purpose, the long-term acoustic monitoring set on the whole French and Australian fleets could provide an effective dataset. Besides, PAM will allow the estimation of odontocetes arrival time on longline. From the intensities of received sounds produced by odontocetes compared to sound emission levels of these species, a transmission model loss should allow a range estimation of the source production. Additionally, displacements of predators could be modelled based upon their swimming speed and the hypothesis that they may head straight to the longline once this one is fishing, to estimate where odontocetes were prior to line setting.

These spatial models should bring insights upon the natural distribution of predators yet poorly known. Although some insights on both species spatial distribution have been brought thanks to photo-identification [START_REF] Tixier | Killer whales of the Crozet islands, photo-identification catalogue[END_REF][START_REF] Labadie | First demographic insights on historically harvested and poorly known male sperm whale populations off the Crozet and Kerguelen Islands (Southern Ocean)[END_REF]. Additionally, a recent analysis underlined large spatio-temporal variations of sperm whale-vessels interaction levels with French and Australian commercial Patagonian toothfish on the Kerguelen Plateau, as detailed in Appendix 5 (Tixier et al. submitted in CCAMLR Sc). The observed spatial gradient of interaction rates raised the question whether this reflect a natural variation in sperm whale distribution through the Kerguelen Plateau or if this behaviour has been learned by ease in some the north and likely transmitted across individuals of populations through social pathways [START_REF] Schakner | Using Models of Social Transmission to Examine the Spread of Longline Depredation Behavior among Sperm Whales in the Gulf of Alaska[END_REF]. Investigation of the spatial factors influencing odontocetes' distribution paired with the understanding of the social behaviour transmission are essential to better predict the occurrence of interactions to implement effective strategies of avoidance in the future.

However, it is important to keep in mind that odontocetes distribution is slightly skewed by observation efforts made from longliners when interactions occur. It is then difficult to clearly determine whether the presence in this area is because of the fishing activity or because of the resource abundance. Therefore, causality between the fishing success and the interaction rates might be difficult to understand, as a good fishing success may occur where individuals naturally forage or could attract depredating species. Within this purpose a PAM approach, such as a multi-month monitoring of acoustic occurrence of cetaceans using an acoustic recording station could be used to assess the importance of fishing grounds for natural foraging of sperm whales and killer whales in absence of any fishing activity. One limitation to use a stationary hydrophone is to monitor one spot and to confound individuals using it. An individual monitoring is therefore essential to precise the habitat use at a finer scale. Bio-logging is then an interesting tool to follow where odontocetes focus their natural foraging effort. Thus further efforts should be increased to deploy loggers, and especially the model with PCA estimation processing on-board described in Chapter 4. Additionally, a comparison between foraging effort and the habitat exploration should highlight cues on the natural diet of these predators, in particular whether toothfish is a natural prey remained unclear. However, an increasing number of cues strongly suggest that both species naturally forage on Patagonian toothfish. Sperm whales has been described to naturally feed on toothfish obtained from stomachs contents of harpooned sperm whales [START_REF] Abe | Notes on fishes from the stomachs of whales taken in the Antarctic. II. On Dissostichus and Ceratias, with an appendix (Japanese names of important Antarctic fishes)[END_REF]. As for killer whales, results from this study showing that individuals equipped with loggers dive to the seafloor at 300 m depth in absence of longlines suggest that they naturally forage on toothfish (Chapter 4). Additionally, an isotopic analysis has been assessed by Tixier et al. (submitted) revealing that Patagonian toothfish is likely a natural prey item in the diet of killer whales at Crozet. There is not so much doubt left about Patagonian toothfish being a natural diet of both predators. Nevertheless, the question is therefore to estimate the importance of the toothfish in these species' diet to assess whether depredation behaviour may impact the fish stock if it added a supplementary predatory pressure on the toothfish population compared to their natural consumption.

Furthermore, a finer-scale analysis using bio-loggers paired with movement data of vessels would allow to assess the extent at which odontocetes modify their natural behaviour in response to fishing operations. More specifically, it is still unknown whether depredation behaviour is an opportunistic behaviour, i.e. odontocetes interact with longlines when their distribution spatio-temporally overlaps with that of the fishing operations, or an active behaviour, i.e. odontocetes follow fishing vessels, or a combination of both. To highlight whether interactions result from opportunistic or active behaviour, a preliminary meta-analysis was conducted between the different Patagonian toothfish fisheries of the Southern Ocean in order to better understand the spatio-temporal variations of interaction rates, as described in Appendix 6 (Tixier et al. submitted in Scientific Report). Besides, the preference towards an opportunistic or an active behaviour should be driven by the most optimal depredation strategy.

Although depredation behaviour is energetically more rewarding than a natural behaviour (Tixier et al. 2015a), at some points the efforts to pursue fishing vessels might be higher than to forage naturally. Assuming that behaviours may evolve to become more active than opportunistic, it would raise the question on these predators' ecosystem health and their capacity to find their resource, urging the necessity of a well-managed fishery leaving sufficient fish resources for their natural predators. In that case the long term monitoring of demographic performances, in particular calving rate and early survival of killer whales, along the body condition monitored through drone aerial picture of both sperm whales and killer whales may reveal if both of these natural predators of Patagonian toothfish are under nutritional stress or not.

CONCLUSION AND PERSPECTIVES

The behavioural ecology approaches of fishermen and odontocetes assessed in this thesis filled gaps in our understanding of the depredation conflicts on demersal longline fisheries. Results may be extrapolated beyond the study cases of Crozet and Kerguelen, and have implications for other Patagonian toothfish demersal longline fisheries in the Southern Ocean, and also for other demersal longline fisheries experiencing depredation in other parts of the world. For instance, the findings on the way odontocetes interact with longlines in the underwater dimension, with interactions occurring during both hauling and soaking phases, may substantially change the way depredation was previously assessed in demersal longline fisheries. Other findings from the novel approaches used in this thesis such as passive acoustic monitoring, bio-logging and human behavioural ecology brought insights on depredation with broad implications, for both other fisheries experiencing this conflict. Although it is impossible to generalise the issue across fisheries as different predators are involved within different environments, all conflicts have in common the human activity. I therefore strongly suggest considering fishermen as actors to be included in ecosystem based studies of depredation in addition to fish and odontocete species. These models based upon the optimal foraging strategies should account for impacts of depredation on fishermen through socio-economic costs. Further studies should also focus on the behaviour of fishermen at fine scales. For instance, the analysis of the behavioural factors influencing the acoustic detectability of captains, which was introduced here, may help to identify easy-to-implement ways to decrease probabilities of interaction with odontocetes. Such analyses will be finalised in a nearby future using data from both Australian and French Patagonian toothfish longliners. Additionally, the acoustic signatures of vessels will be investigated on the demersal longline fisheries targeting the blue-eye trevalla around St Paul and Amsterdam Islands and in Southeast Australia.

The PAM approach developed in this thesis therefore proved to be a useful tool to investigate both human and odontocetes behaviour. To study odontocete behaviour, which understanding still needs to be improved for mitigation, conservation and general ecology purposes, PAM should be combined with other approaches. For example, using information on depredating odontocete individuals, long-term studies can help to identify the mechanisms leading predators to switch from natural to depredation behaviour. Although the identification of these mechanisms may constitute an important step to find appropriate mitigation measures, as described in Appendixes 5 and 6; it could also help to predict the possibility of depredation to develop in other regions and/or fisheries. This might be the case of the Greenlandic halibut fishery where fishermen and killer whales foraging activity co-occurrence in space is growing with the decrease of the sea-ice range, as detailed in Appendix 7 [START_REF] Lennert | At the cutting edge of the future: Unravelling depredation, behaviour and movement of killer whales in the act of flexible management regimes in Arctic Greenland[END_REF].

This forecasting study emphasizes the importance to share the knowledge as a better understanding of the conflicts' mechanisms may also prevent depredation. Thus, collaborative work should increase as depicted in Appendixes 5 and 6. Within this purpose OrcaDepred also includes the pelagic longline fishery targeting swordfish and tuna around Reunion Island to broaden the expertise.

In a further extent, this thesis revealed the importance to use a behavioural ecology approach for both human and wildlife to assess a conservation conflict [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Blackwell | No single solution: application of behavioural principles in mitigating human-wildlife conflict[END_REF]. Assessing the impact of the human impacts on wildlife but also the wildlife impacts of human is crucial to tend toward a win-win solution [START_REF] Redpath | Understanding and managing conservation conflicts[END_REF]. Indeed, conservation conflict's solution should be a situation where both parties can coexist with a decrease of the human-wildlife impact [START_REF] Treves | Co-Managing Human-Wildlife Conflicts: A Review[END_REF][START_REF] Redpath | Understanding and managing conservation conflicts[END_REF][START_REF] Sarrazin | Evolution in the Anthropocene[END_REF]. Therefore, this thesis with all collaborative works of the OrcaDepred project aim altogether at determining and contributing to the sustainability of the longlines fisheries within a depredation context. The goal is to conciliate a cost-effective activity for fishermen with the longline, one of the most fish-selective methods available to date, while preserving the fish stock and allowing the odontocetes populations to be maintained using natural prey resources. A mitigation solution cannot come from a simple and isolated measure. The knowledge garnered by scientific studies is crucial to lead in the good direction countermeasure research, but the work must be conducted with industrial providers and in collaboration with fishermen, fishing companies and the policy makers.

Supp. data 2 -Akaike Information Criterions of the generalized linear mixed models of the probability of captains to leave in relation to the fishing success and the competition for both Crozet and Kerguelen. Best AIC are in bold. The spectrograms were made over 1 min for vessels at 1 km for the hydrophone, with a colour scale representing the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). The power spectrum (in dB re 1 µPa) were assessed through PAM guide with batched recording files of 3 min for vessels at 1 km of the hydrophone [START_REF] Merchant | Measuring acoustic habitats[END_REF]. See Supplementary data for the 3 other vessels. Voir avec les marins pour la télécommande ou avec le capitaine pour l'heure de mise à l'eau.

Models

LIST OF FIGURES

Recording Schedule

Décocher "Periodic Recording" Permets de sélectionner un enregistrement cyclique ou continu.

Audio Options

Preamp Gain

Cocher: HIGH High pass filter.

Cocher: OFF

Sample Rate

Cocher: 96 kHz Cette configuration permet de couvrir les bandes de fréquences des sons /!\ Ne pas oublier de remplir la fiche déploiement

Système d'attache

Pour venir fixer l'hydrophone à l'orin de la palangre il faudra au préalable préparer un système d'accroche sur le Soundtrap (Figure 3). Prendre un bout d'orin d'1m de long et de petit diamètre de préférence, mettre 2 mousquetons aux extrémités enfermés dans des boucles/yeux. Venir fixer l'hydrophone au centre. Sur la Figure 3 est proposé un système mais si vous pensez à d'autres systèmes plus fiable n'hésitez pas, les marins sont de bon conseils. Mais attention, bien maintenir l'hydrophone contre la corde, le plus sûr étant de venir sécuriser l'attache avec du duck tape/ scotch, ne pas hésiter à faire plusieurs tours pour bien consolider la fixation.

Ce système d'attache sera permanent et viendra se fixer/retirer sur l'orin d'une palangre à 100m de profondeur sous la bouée (Figure 4). Le plus simple étant de préparer une section d'orin qui sera dédié à l'accroche de l'hydrophone et qui viendra en premier en dessous de la bouée. La plupart des navires possèdent des sections de 200m, mais cela peut varier. Sur ce bout d'orin, préparer des yeux à 100m de profondeur et espacé d'environ 1m pour venir fixer l'hydrophone via les mousquetons.

IMPORTANT: éviter toute liaison metal/metal (e.g. deux mousquetons consécutifs) qui produirait des bruits "clic-clic" parasites.

  following about the thesis entitled Behavioural ecology of fishermen and odontocetes in a depredation context submitted for the degree of Doctor of Philosophy a. I am the creator of all or part of the whole work(s) (including content and layout) and that where reference is made to the work of others, due acknowledgement is given. b. The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or other rights whatsoever of any person. c. That if the work(s) have been commissioned, sponsored or supported by any organisation, I have fulfilled all of the obligations required by such contract of agreement. d. That any material in the thesis which has been accepted for a degree or diploma by any university is identified in the text. e. All research integrity requirements have been complied with.

  Fondation d'Entreprise des Mers australes, the Syndicat des Armements Réunionais des Palangriers Congélateurs, fishing companies, the Direction des Pêche Maritimes et de l'Aquaculture, Terres Australes et Antarctiques Françaises (the Natural Reserve and Fishery units) and the Australian Research Council grant. All instrument deployments followed the ethics policies of the Terres Australes et Antarctiques Françaises (TAAF) and were authorized by the Réserve Naturelle Nationale (RNN des TAAF) through approval A-2017-154. The work carried out was also in agreement with Deakin University Animal Welfare Committee (Permit No. A71-2011; B12-2013) and in accordance with the regulations of Department of Environment, Land, Water and Planning (Wildlife Research Permits 1005848; 1007153).



  Collet, J., G. Richard, A. Janc, C. Guinet, and H. Weimerskirch. 2018 Influence of depredating cetaceans on albatross attraction and attendance patterns at fishing boats. Marine Ecology Progress Series 605. doi: 10.3354/meps12757 this article was realized apart from my PhD with my colleague Julien Collet, I have contributed to the design of the analyses and helped in the drafting of the manuscript Poster at the French workshop SERENADE organized in the context of the Sea Tech Week in Brest from the 11th to the 13th of October 2016.

2016

  Talk at the Festival International du Film Ornithologique, in Ménigoute, October 2016  Stand at the Fête de la science, Museum of La Rochelle, October 2017  Webinar for primary school students during the Polar week with APECS-France, October 2017 Talk at the guinguette La Belle du Gabut for the TPs sandwich, La Rochelle, August 2018  Organisation of the 13th student meeting: Ecology and Behaviour, Vice-treasurer of the AREB, at Centre d'Etudes Biologiques de Chizé, 2017

Figure 1 - 1 -

 11 Figure 1-1 -Fishing phases of a demersal longline.

Figure 1 - 2 -

 12 Figure 1-2 -Map from the Coalition of Legal Toothfish Operators (COLTO) of the toothfish fisheries sites with their total allowable catches (TACs), for the season 2015-2016, set by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) or by national authorities within their Economic Exclusive Zone (EEZ). The red square highlights French EEZs of Crozet and Kerguelen islands on which this thesis focused.

  This example of depredation issue has been studied since 2007 by the research project named OrcaDepred. This project also assesses depredation by cetaceans on pelagic longline fishery targeting swordfish and tuna around Reunion Island and on demersal longline fishery fishing the blue-eye trevalla around St Paul and Amsterdam Islands. The project is led by Dr. Christophe Guinet (CEBC, CNRS-University of La Rochelle, France) and involved 8 partnerinstitutions (CEBC-CNRS, MNHN, ENSTA Bretagne, UBO, IFREMER, UMI 2958 GT-CNRS, AMURE, IRD UMR MARBEC) as well as one industrial provider (SATIM). OrcaDepred is supported by the ANR, the Fondation d'Entreprise des Mers australes, the Syndicat des Armements Réunionais des Palangriers Congélateurs, fishing companies, the Direction des Pêche Maritimes et de l'Aquaculture, Terres Australes et Antarctiques Françaises (TAAF : the Natural Reserve and Fishery unit).

  rates. Using the PECHKER dataset (Museum d'Histoire Naturelle de Paris; Martin and Pruvost 2007), I assessed the mean daily interaction rate per trip with killer and/or sperm whales for 22 captains whom had operated on 8 different vessels between January 2008 and July 2015 at Crozet and Kerguelen, represented on Figure 2-1.

- 1 inter

 1 -captains variability for a same vessel, e.g. between captains lighter green, darker green and purple for vessels 5 with sperm whales at Kerguelen. This variability suggests that captains' fishing strategies may involve different interaction rates. The purpose is then to determine what decisions made by captains, and therefore what fishing strategies, may induce or avoid depredation.

Figure 2 - 1 -

 21 Figure 2-1 -Boxplot of the mean daily interaction rates per trip for every captain (colour) with a given fishing vessels.

Figure 2 - 2 -

 22 Figure 2-2 -Map of Crozet and Kerguelen Islands, southern Indian Ocean, with fishing efforts, i.e. all longlines, positions (orange). The map was realised with the package marmap through the software R.

EQUATION 2 - 4 :

 24 α +β1Fishing success + β2 (Competition or Nb individuals) + β3Fishing success x (Competition or Nb individuals)+ε = constant Since only the number of cetaceans can vary, we aimed at resolving:  (β2+ β3Fishing success) x (Competition or Nb individuals)= constant β2+ β3Fishing success = 0 2.2.4. RESULTS 2.2.4.1. HAULING SESSIONS Data used in this study included 1,241 hauling sessions within the Crozet EEZ and 4,302 hauling sessions within the Kerguelen EEZ (Figure 2-3 and Supp. data 1).The competition at Crozet was considered either in presence of killer whales alone, in presence of sperm whales alone, or in presence of both species simultaneously (Figure2-3& Supp. data 1). At Kerguelen, we only considered sessions with sperm whales alone, since the number of sessions in presence of killer whales only (6) and the number of sessions in presence of both species simultaneously (19) were both too small to conduct statistical analyses (Figure2-3 & Supp. data 1).

Figure 2 - 3 -

 23 Figure 2-3 -Number of sessions in both EEZs with decision to stay or leave, in absence of cetacean, or in presence of cetaceans (each species alone or both simultaneously).

Figure 2 - 4 -

 24 Figure 2-4 -Distributions of the distance after hauling sessions with piecewise regressions fitted to determine the distance threshold to consider a vessel 'leaving' in Crozet and Kerguelen.

  ), both at Crozet (Figure 2-5.a) and Kerguelen (Figure 2-5.b.). As a result, for the 14 captains who spent more than 2 seasons both at Crozet and Kerguelen, we obtained a correlation of 0.74 between the slopes (β1+bj) at Crozet and at Kerguelen, (Pearson's test: t=3.79, p=0.003, Figure

Figure 2 -

 2 5.a. & Table2-1). Similarly, when sperm whales were the only depredating species, the probability of captains to leave a patch was nearly 1.5 times higher than the probability to leave in absence of cetacean both at Crozet (z=2.10, p=0.04, Figure 2-5.a. & Table 2-1) and at Kerguelen (z=6.06, p<0.001, Figure 2-5.b. & Table

Figure 2 - 5 -

 25 Figure 2-5 -Probabilities to leave for captains, π, in response to the variation of the standardized toothfish mass caught per day and the competition variable, with the variability between captains in orange (Table2-1). The probability to leave in absence of cetaceans is represented in red, in the presence of killer whales only in light green, in the presence of sperm whales in dark green, and in the presence of both species simultaneously in blue.

Figure 2 -

 2 Figure 2-6 -Linear regression between the slope (β1 + bj) of the probability to leave as a function of the fishing success at Crozet and the one at Kerguelen for each captain j (R²=0.54). We considered only captains who spent more than 2 seasons both at Crozet and Kerguelen.

EQUATION 2 -

 2 8 : logit(πij)= α + aj +β1Fishing success ij + (β2+cj)Nb sperm whalesij +ε ij whereas at Kerguelen the random effect was obtained over the slope of the fishing success (Supp. data 3): EQUATION 2-9 : logit(πij)= α + aj +(β1+bj)Fishing success ij + β2Nb sperm whalesij +ε ij

  2, Figure 2-7 b & c). The slope of the relationship between the daily mass of fish caught and the probability to leave decreased when the number of cetaceans increased (Figure 2-7.b.). This result suggests that when the fishing success increased in presence of a high number of cetaceans, the probability to leave decreased at a faster rate than when the fishing success increased with a few cetaceans interacting (Figure2-7.b.). Conversely, the slope of the relationship between the number of cetaceans and the probability to leave decreased when the daily mass of fish caught increased Figure2-7.c.).

Figure 2 -

 2 Figure 2-7 -a) Probability to leave a ground for captains, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number of cetaceans (green). Lighter thin lines represent captains' probabilities to leave on the left box. b&c) Dashed lines represent the interaction results (see Table2-2), showing the probabilities to leave a ground as a function of one of the two standardized variable, and the color's gradients of dashed lines from light to dark represent variation of the other variable from minimum value to maximum value. b) The lightest orange dashed lines represent the probability to leave a ground as a function of fishing success the minimum number of cetaceans (i.e. in absence), and the darkest orange dashed line for the maximum number of cetaceans (i.e. 29 individuals). c) The lightest green dashed lines represent the probability to leave a ground as a function of the number of cetaceans for the minimum fishing success (i.e. a null success), and the darkest green dashed line for the maximum fishing success (i.e. 15650 kg).

Figure 2 -

 2 Figure 2-8 -Probabilities to leave a ground, π, at Crozet (a) and at Kerguelen (b) in response to the variation of the standardized fishing success (orange) and the standardized number of sperm whales (green). Lighter thin lines represent variability between captains (see Table2-2).

Figure 2 -

 2 Figure 2-9 -Probability to leave a ground, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number of killer whales (green), (Table 2-2). The dashed

2. 2

 2 .4.4. DESCRIPTION OF A FISHING SUCCESS' THRESHOLD FOR CAPTAINS' DECISION By assessing the intersection between smoothed distributions of the fishing success, for both hauling sessions followed by a decision to stay and sessions followed by a decision to leave, we obtained a threshold of approximately 500kg.day -1 at Crozet (Figure 2-10.a) and 2,700kg.day -1 at Kerguelen (Figure 2-10.b). These results suggest that below these mean thresholds, captains were more likely to leave their patch.

Figure 2 -

 2 Figure 2-10 -Distributions of the weight of toothfish caught per day for every session, both at Crozet (a) and Kerguelen (b), followed both by a decision to stay (light grey) and by a decision to leave (black), with smoothing splines (coefficient=0.5). On the top we represented the global distributions and on the bottom we showed a zoom of the first values, allowing us to assess the threshold of the fishing success determining the captains' decision to stay or leave.

EQUATION 2 -

 2 Figure 2-11 -Histograms of the optimal distance score (Equation 2-11) for all captains both at Crozet and Kerguelen.

EQUATION 2 -

 2 12 : logit(πij)= α + aj +(β1+bj)Fishing success ij + β2Competition ij + β3Fishing success ij x Competition ij +ε ij

Figure 2 -

 2 Figure 2-12) whereas the optimal distance score in presence of sperm whales alone was not significantly different than in absence of odontocetes (pkw =0.146, Figure 2-12). When both species were present, the difference with the score in absence of odontocete was close to be significant (pboth sp =0.07, Figure 2-12).

Figure 2 -

 2 Figure 2-12-Boxplot of the optimal distance score per odontocete occurrence between Crozet and Kerguelen with mean scores represented by the coloured triangles and captains' variability by the grey dots.

Figure 2 -

 2 12 (right panel). Similarly, no significant differences between the scores in absence of odontocete, in red on, and in presence of sperm whales alone, in dark green on Figure2-12, was observed at Crozet. Besides the estimates revealed that in absence of odontocetes captains are more likely to choose the closest distances after 79% of the longlines hauled, in red on Figure2-12, whereas in the presence of killer whales optimality decreases to 68% of the longlines, in light green on Figure2-12. Additionally the inter-captains variability of optimal distance scores strengthen the hypothesis that they do not perceive their environment similarly and have different sensitivity to the competition (see captains variability in presence of killer whales on

EQUATION 2 -

 2 14: 𝒅 𝒆 = 𝒅 𝒓 -𝒎𝒊𝒏 ( 𝒅 𝒑 ) I then estimated the mean (± sd) extra distance covered in presence of odontocetes at Crozet that I compared to the mean extra distance covered in absence of odontocete, as a reference of non-optimality in captains' hauling decision due to other parameters. I obtained extra distances of: 8.9±13.4 km/longline in absence of cetacean, 11.1±16.8 km/longline in presence of both species, 11.2±15.6 km/longline in presence of sperm whales alone and 13.5±20.4 km/longline

Figure 2 -

 2 Figure 2-13 -Boxplot of the extra distance covered after a longline when captains do not chose the optimal decision (i.e. optimal distance score<1) per cetacean occurrence at Crozet, with mean values represented by the coloured triangles.

odontocetes

  Our studies showed the necessity to work at individual scale to assess captains' fishing strategies. Differences of personal sensitivity of the competition by captains, as revealed on Figure 2-5, Figure 2-7 and Figure 2-8 may explain a part of their inter-individual variation of interaction rates depicted on Figure 2-1. Thus, these results suggest that some captains may display foraging strategies which are more sensitive to interaction with odontocetes than strategies of other captains. Apart from the fishing practices, we may wonder what type of behaviours may change the detectability of captains. As odontocetes mostly rely on acoustic to forage, captains could also display different navigation behaviours. As previously hypothesised, the acoustic

- 1 .

 1 After a preliminary test within the Brest bay to train manipulating the array, I worked with RTSYS and the captains of the French longliners to find an alternative mooring system more adapted to the fishing context. The array was then deployed on longliners during a first field mission from end December 2016 to early March 2017. The array was attached on the downline connecting the buoy to the ballast of a longline as represented on the Figure3-1. The whole system was actually too complex to be conveniently deployed in the Southern Ocean from a fishing vessel that also conducts fishing operation. The use of the 100 m-cables added logistical constraints and was time consuming to recover. I have then switched to a simplest model with two hydrophones connected with 10 m cables. Using 2 hydrophones instead of 4 can still be useful for acoustic tracking, especially to localise sperm whales using the difference of arrival time between a click and its echo (see Appendix 1). Nevertheless, I deployed the array 4 times with 4 hydrophones and then 13 times with 2 hydrophones, for a total recording duration of 656 h, as revealed in the first part of Table3-1.

Figure 3 - 1 -

 31 Figure 3-1-Scheme of the acoustic array designed by RTSYS deployed during the first mission (2016-2017).

Figure 3 - 2 -

 32 Figure 3-2 -Picture of the attachment of the SoundTrap on the downline (green rope). The SoundTrap is set permanently on a small rope (the blue one) to facilitate the deployment on the downline.

  -2 and where to deploy the hydrophone on the longline as depicted on Figure3-3. The single hydrophone is set at 100 m to avoid strong noise from sea surface. A consistency between all deployments will allow comparing acoustic recordings between vessels. The protocol advises deploying as often as possible the hydrophone with a continuous recording, without adding excessive work for both fishermen and fishery observer, whom also download the data from the SoundTrap on-board once recovered. During the deployment, information such as environmental conditions, observation of odontocetes and occurrence of navigation manoeuvres made by the captains are asked to be notice by fishermen and fishery observers within a deployment sheet, provided at the end of Appendix 3.

Figure 3 - 3 -

 33 Figure 3-3 -Position of the recorder during deployment, that is attached to a piece of downline under the buoy.

Figure 3 - 4 .

 34 Figure 3-4. For instance, a group of killer whales was first visually observed interacting

Figure 3 - 4 -

 34 Figure 3-4 -Spectrogram of sperm whales' clicks (broadband frequencies produced during a very short duration) and killer stereotyped calls (e.g. at 7 s and 9 s). The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). The recording was made during the first mission, the 27 th of in February 2017 on the vessel Ile de La Réunion at Crozet.

Figure 3 - 5 -

 35  and power spectrum densities (right panels) of 2 French (vessel #1 and vessel #2) and 1 Australian (vessel #3) longliners (one vessel per line). The spectrograms were made over 1 min for vessels at 1 km for the hydrophone, with a colour scale representing the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). The power spectrum (in dB re 1 µPa) were assessed through PAM guide with batched recording files of 3 min for vessels at 1 km of the hydrophone[START_REF] Merchant | Measuring acoustic habitats[END_REF]. See Supplementary data for the 3 other vessels.

EQUATION 3 - 1 :

 31 SL=RL+TLTransmission loss is the attenuation of sound intensity as the sound propagates underwater. I used a transmission loss model considering the combination between the loss due to geometrical spreading of the sound wave-front and the absorption loss due to propagation[START_REF] Fisher | Sound absorption in sea water[END_REF] Simmons 1977, Ainslie and[START_REF] Ainslie | A simplified formula for viscous and chemical absorption in sea water[END_REF]. A simple model for underwater geometrical spreading is a spherical propagation until waves reach the surface and then follows a cylindrical propagation as they are constraint by the limit of the water column. Thus, I considered the transition range from which the sound switches from spherical to cylindrical spreading as onehalf of the channel depth, H[START_REF] Urick | Principles of underwater sound[END_REF]. The absorption loss is sound's frequency dependent[START_REF] Fisher | Sound absorption in sea water[END_REF] Simmons 1977, Ainslie and[START_REF] Ainslie | A simplified formula for viscous and chemical absorption in sea water[END_REF]. The total transmission loss model was then defined as: (𝑹) + α(f)R with R the range source-hydrophone, H the depth of the water column, α the absorption component and f the sound frequency and Sc set at 10 or 17 to test a more permissive vs a more rigourous attenuation model through the cylindrical spreading (Sc.log10(R)).

Figure 3 -

 3 Figure 3-6 -Spectrogram of the boat ending its setting (until 38 s) and moving forward (defined as 'post-setting'). The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). Clicks of sperm whales are also present (broadband frequencies produced during a very short duration).

Figure 3 -

 3 Figure 3-7-Spectrogram of the boat hauling a longline with two backward manoeuvres at 8 s and 21 s of the recording. The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ).

  ) and was then sent to Wildlife Computers during this PhD to be implemented into new loggers. On an experimental point of view, I personally deployed bio-loggers during my second field mission, and subsequently analysed their data. In particular, I deployed 4 of the new loggers with my algorithm implemented by Wildlife Computers, but none emitted any signal. This failure is currently investigating by Wildlife Computers. I also deployed 4 classical ARGOS tracking loggers (SPLASH 10 and SPOT 5 from Wildlife Computers) on killer whales. One of the loggers remained only a few hours on the individual because the anchors were not pegged deep enough on the individual's skin. Among the 3 loggers correctly deployed, two had pressure sensors (SPLASH 10) and were analysed in this thesis paired with the data provided by accelerometer/pressure sensor loggers (section 4.3).

EQUATION 4 - 1

 41 𝒅(𝒕) =𝝈[𝒂 𝒙(𝒕)]+𝝈[𝒂 𝒚(𝒕)]+𝝈[𝒂 𝒛 (𝒕)] 𝟑 with ax, ay and az the acceleration value (in g) along the x, y and z axes, and 𝜎[𝑎 𝑖 (𝑡)] the standard deviation of acceleration (ai) for time between t-1.5 to t+1.5 seconds (i.e. during 3 s).

Figure 4 - 1 -

 41 Figure 4-1 -Distribution of d (Equation 4-1) centred on 0: dcentred(t)= d(t)-mean(d).

Figure 4 - 2 -

 42 Figure 4-2 -Example of prey catch attempts estimations, in red, over a dive profile with buzzes events, in green (top panel). Value of d(t) over the dive, with the over-dispersed values considered as PCA in red (middle panel). Distribution of d, with the threshold (|min(d)|) estimation represented by the red line (bottom panel).

Figure 4 - 3 -

 43 Figure 4-3 -Regression between the number of buzzes and the number of prey catch attempts estimated by our method. The mixed linear model equation (black line) was: Bij=0.46x Pij +(2.9±6.2).

Figure 4 - 4 -

 44 Figure 4-4 -Estimation of the number of buzzes (green) for each individual from the linear regression (dark line) estimated by the 5 other individuals (blue). Equation for each regression with the slope's pvalue were noticed on each graph. The numbers of PCA of each individual not considered in the regression were represented in red. The estimated number of buzzes for each individual (green) should follow the equation y=x (grey line) for a correct estimation.

Figure 4 - 5 -

 45 Figure 4-5 -Histograms of association (%) between buzz and PCA for the 6 individuals' dives.

Figure 4 -

 4 Figure 4-6 -Map of the study area with the locations of the fishing activity (orange dots) around Crozet and Kerguelen. The green rectangle indicates the seamount where the deployment of instruments on killer whales occurred.

  TRACKING DATA LOGGERS Only two loggers of the six deployed transmitted correctly. The other 4 loggers failed to transmit, most likely because of an on-board software issue. The two operational loggers were deployed on two adult female killer whales in February 2018 near the seamount located 40 km south-east from East Island, Crozet Archipelago (Figure 4-6). The two instruments provided 28 and 65 ARGOS locations during 3 and 7 days, respectively, before they stopped transmitting (i.e. fell off the animal or battery expired). Of these locations, 20 and 31, respectively, had an accuracy estimates and, thus, were included in further analyses. Out of the 20 useful locations from the first logger, 9 locations were identified as "interactions" with 5 different longlines. For the second logger, 14 points where identified as "interactions" with 11 different longlines. Among the 'interactions' positions of the first individual, 3 were recorded during the hauling of 3 different longlines and 6 were recorded as overlapping with 2 different longlines during soaking. For the second individual, 6 positions 4. BIO-LOGGING 124 were recorded during the hauling of 5 different longlines and 8 positions overlapped with 6 different soaking longlines.

Figure 4 -

 4 Figure 4-7 -ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel). The ARGOS position is indicated on the map by the red circle with the diameter representing the location estimate error buffer (cf. CLS classes). The color shade depicted the bathymetry. The soaking longlines are shown in black and the longline being hauled is shown in red at the given transmission time of the ARGOS position (left panel). The dive profile assessed the depth range estimated by the tag through the thickness of the drawing (right panel). On the dive profile the transmission time of the ARGOS position is represented by the red triangle and the bathymetry recorded by the boat during the hauling session is indicated by the red line, as the killer whale was interacting with the boat the red line represented then the bathymetry below the individual for a given time (right panel).

  -8). The next position was recorded 20 h later and at 4 km from the last position of previous series.During this 20 h time window, the killer whale conducted 8 dives deeper than 450 m in <6 h, with 5 of these being consecutive dives to the same estimated depth (502 ± 22 m) within 2 h (Figure4-8). These dive depths correspond to the bathymetry at the extremity of the closest longline (set at 515 m; Figure4-8). All these events occurred around the soaking longlines (i.e. the ARGOS positions and the recorded dives within the 15 h window; Figure4-8) while no vessel was in the area. Indeed, after setting the longlines, the fishing vessel left the area and traveled 140 km away. It then returned to haul the considered longline, 3 h after the last deep dive (502 ± 22 m) was recorded. In addition, no other fishing vessel was active in this sector.

Figure 4 -

 4 Figure 4-8 -ARGOS positions (left panel) of an 'interaction before hauling' with their associate dive profile (right panel) within a 15 hour-window. The ARGOS positions are indicated on the map by the red circles (numbered chronologically along track) with the diameter representing the location estimate error buffer (cf. CLS classes). The soaking longlines are indicated by the black lines on the map, and the closest soaking longlines to the most recent transmitted positions are in green (left panel). The dive profile assessed the depth range estimated by the tag through the thickness of the drawing (right panel). On the dive profile, the transmission times of the ARGOS positions are represented by the red triangles and the bathymetry of the closest longlines (at their extremities) at the time of the most recent ARGOS determined positions are outlined in green (right panel).

Figure 4 -

 4 Figure 4-9 -ARGOS positions (left panel) within a 3 hour-window of 'no interaction' with the associate dive profile (right panel). The ARGOS positions are represented on the map by the red buffers with the diameter standing for the estimate error (cf. CLS classes) and the numbers assessed the chronology of the track. The longlines at sea during these 3 hours are plotted in black on the map (left panel).. The dive profile assessed the depth range estimated by the tag through the thickness of the drawing. On the dive profile the four transmission times of the ARGOS positions are represented by the red triangles (right panel).

Figure 4 -

 4 Figure 4-10 -Boxplots of the mean acceleration norm recorded during the bottom phase on accelerometers with the closest distance and for the two different season.

4 -

 4 11). The first event in Kerguelen occurred 1 h before the arrival of the fishing vessel at the longline (Figure 4-11.A.B.). The second event happened just after fishermen stopped hauling the longline half way through and let it fall back to the seafloor (Figure 4-11.C.D.). The third event occurred 1 day after the longline was set and 3 days before the hauling (Figure 4-11.E.F.). During these events, longlines were elevated by 30 m, 40 m and 300 m, respectively, above the seafloor (Figure 4-11). Sperm whale presence was confirmed on the first event (Figure 4-11.A.B.) by visual observations and passive acoustic recordings (obtained as part of concurrent studies). No such cues of sperm whale presence were detected near the set during the second event, though no passive acoustic monitoring occurred in the area at that time (Crozet 2016-2017, Figure 4-11.C.D.

Figure 4 -

 4 Figure 4-11 -Dive profiles (A,C,E) of three accelerometers hauled without any fish and showing some depth anomalies, zoomed on the right column (B,D,F). Each row represent a different logger monitoring a precise event: the first line represents the event which occurred at Kerguelen during the first field season (2016-2017); the second line represents the event which occurred at Crozet the same field season (2016-2017); and the third line represents the event with the dead sperm whale hauled at Crozet in February 2018. We assessed on the depth profiles and elevation zooms the arrival time of the fishing boat on the longline (red line), and for the second event (D) we also monitored the time at which fishermen stopped the fishing activity and cut the longline (blue line) before leaving.

(

  Kerguelen 1617) was too low to indicate a capture on the equipped hook, suggesting some activity further away (Figure 4-12).

Figure 4 -

 4 Figure 4-12 -The first row depicts acceleration norm over the dive of the two equipped hooks showing activity before the elevation event (green line). The mean acceleration norm before the elevation event (green line) was compared for each accelerometer with mean acceleration norm estimated with the distance of the closest toothfish capture.

  observers operating from the vessel, and to the Museum National d'Histoire Naturelle de Paris for providing access to the PechKer Database. This study was part of the OrcaDepred program supported by the ANR, the Fondation d'Entreprise des Mers australes, the Syndicat des Armements Réunionais des Palangriers Congélateurs, fishing companies, the Direction des Pêche Maritimes et de l'Aquaculture, Terres Australes et Antarctiques Françaises (the Natural Reserve and Fishery units) and the Australian Research Council grant. The first author was financially supported by a departmental and regional grant (Deux-Sèvres and Poitou-Charentes).

Figure 5 - 1 -

 51 Figure 5-1 -Pictures of the protection device deployed during my second field work. a) device close with its attach system; b) system open with the position of the hook baited; c) system set on the longline and ready to be deployed by the window from which longline is dropped off; d) system dropped off the window at the rear side of the longliners during the setting; d) system being hauled at the bunker.

Figure

  Figure 3-4, suggested that they are more likely to wait for the longline being hauled to

Figure 1 - 1 -Figure 1 - 2 -Figure 2 - 1 -Figure 2 - 2 -Figure 2 - 3 -

 1112212223 Figure 1-1 -Fishing phases of a demersal longline. ............................................................................................ 22 Figure 1-2 -Map from the Coalition of Legal Toothfish Operators (COLTO) of the toothfish fisheries sites with their total allowable catches (TACs), for the season 2015-2016, set by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) or by national authorities within their Economic Exclusive Zone (EEZ). The red square highlights French EEZs of Crozet and Kerguelen islands on which this thesis focused. . 28 Figure 2-1 -Boxplot of the mean daily interaction rates per trip for every captain (colour) with a given fishing vessels. .................................................................................................................................................................. 42 Figure 2-2 -Map of Crozet and Kerguelen Islands, southern Indian Ocean, with fishing efforts, i.e. all longlines, positions (in orange). The map was realised with the package marmap through the software R. ........................ 48 Figure 2-3 -Number of sessions in both EEZs with decision to stay or leave, in absence of cetacean, or in presence of cetaceans (each species alone or both simultaneously). ................................................................................... 53 Figure 2-4 -Distributions of the distance after hauling sessions with piecewise regressions fitted to determine the distance threshold to consider a vessel 'leaving' in Crozet and Kerguelen. ......................................................... 54 Figure 2-5 -Probabilities to leave for captains, π, in response to the variation of the standardized toothfish mass caught per day and the competition variable, with the variability between captains in orange (Table2-1). The probability to leave in absence of cetaceans is represented in red, in the presence of killer whales only in light green, in the presence of sperm whales in dark green, and in the presence of both species simultaneously in blue. .............................................................................................................................................................................. 56 Figure 2-6 -Linear regression between the slope (β1 + bj) of the probability to leave as a function of the fishing success at Crozet and the one at Kerguelen for each captain j (R²=0.54). We considered only captains who spent more than 2 seasons both at Crozet and Kerguelen. ............................................................................................. 56 Figure 2-7 -a) Probability to leave a ground for captains, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number ofcetaceans (green). Lighter thin lines represent captains' probabilities to leave on the left box. b&c) Dashed lines represent the interaction results (see Table 2-2), showing the probabilities to leave a ground as a function of one of the two standardized variable, and the color's gradients of dashed lines from light to dark represent variation of the other variable from minimum value to maximum value. b) The lightest orange dashed lines represent the probability to leave a ground as a function of fishing success the minimum number of cetaceans (i.e. in absence), and the darkest orange dashed line for the maximum number of cetaceans (i.e. 29 individuals). c)The lightest green dashed lines represent the probability to leave a ground as a function of the number of cetaceans for the minimum fishing success (i.e. a null success), and the darkest green dashed line for the maximum fishing success (i.e. 15650 kg). ............................ 60 Figure2-8 -Probabilities to leave a ground, π, at Crozet (a) and at Kerguelen (b) in response to the variation of the standardized fishing success (orange) and the standardized number of sperm whales (green). Lighter thin lines represent variability between captains (see Table2-2). ....................................................................................... 61 Figure 2-9 -Probability to leave a ground, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number of killer whales (green), (Table2-2). The dashed line represents non-significant relationship (see Table2-2). ........................................................................................................ 61 Figure 2-10 -Distributions of the weight of toothfish caught per day for every session, both at Crozet (a) and Kerguelen (b), followed both by a decision to stay (light grey) and by a decision to leave (black), with smoothing splines (coefficient=0.5). On the top we represented the global distributions and on the bottom we showed a zoom of the first values, allowing us to assess the threshold of the fishing success determining the captains' decision to stay or leave. ......................................................................................................................................................... 62 Figure 2-11 -Histograms of the optimal distance score (Equation 2-11) for all captains both at Crozet and Kerguelen. ............................................................................................................................................................. 71 Figure 2-12-Boxplot of the optimal distance score per odontocete occurrence between Crozet and Kerguelen with mean scores represented by the coloured triangles and captains' variability by the grey dots. ........................... 73

Figure 2 -Figure 3 - 1 -Figure 3 - 2 -Figure 3 - 3 -Figure 3 - 4 -

 231323334 Figure 1-1 -Fishing phases of a demersal longline. ............................................................................................ 22 Figure 1-2 -Map from the Coalition of Legal Toothfish Operators (COLTO) of the toothfish fisheries sites with their total allowable catches (TACs), for the season 2015-2016, set by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR) or by national authorities within their Economic Exclusive Zone (EEZ). The red square highlights French EEZs of Crozet and Kerguelen islands on which this thesis focused. . 28 Figure 2-1 -Boxplot of the mean daily interaction rates per trip for every captain (colour) with a given fishing vessels. .................................................................................................................................................................. 42 Figure 2-2 -Map of Crozet and Kerguelen Islands, southern Indian Ocean, with fishing efforts, i.e. all longlines, positions (in orange). The map was realised with the package marmap through the software R. ........................ 48 Figure 2-3 -Number of sessions in both EEZs with decision to stay or leave, in absence of cetacean, or in presence of cetaceans (each species alone or both simultaneously). ................................................................................... 53 Figure 2-4 -Distributions of the distance after hauling sessions with piecewise regressions fitted to determine the distance threshold to consider a vessel 'leaving' in Crozet and Kerguelen. ......................................................... 54 Figure 2-5 -Probabilities to leave for captains, π, in response to the variation of the standardized toothfish mass caught per day and the competition variable, with the variability between captains in orange (Table2-1). The probability to leave in absence of cetaceans is represented in red, in the presence of killer whales only in light green, in the presence of sperm whales in dark green, and in the presence of both species simultaneously in blue. .............................................................................................................................................................................. 56 Figure 2-6 -Linear regression between the slope (β1 + bj) of the probability to leave as a function of the fishing success at Crozet and the one at Kerguelen for each captain j (R²=0.54). We considered only captains who spent more than 2 seasons both at Crozet and Kerguelen. ............................................................................................. 56 Figure 2-7 -a) Probability to leave a ground for captains, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number ofcetaceans (green). Lighter thin lines represent captains' probabilities to leave on the left box. b&c) Dashed lines represent the interaction results (see Table 2-2), showing the probabilities to leave a ground as a function of one of the two standardized variable, and the color's gradients of dashed lines from light to dark represent variation of the other variable from minimum value to maximum value. b) The lightest orange dashed lines represent the probability to leave a ground as a function of fishing success the minimum number of cetaceans (i.e. in absence), and the darkest orange dashed line for the maximum number of cetaceans (i.e. 29 individuals). c)The lightest green dashed lines represent the probability to leave a ground as a function of the number of cetaceans for the minimum fishing success (i.e. a null success), and the darkest green dashed line for the maximum fishing success (i.e. 15650 kg). ............................ 60 Figure2-8 -Probabilities to leave a ground, π, at Crozet (a) and at Kerguelen (b) in response to the variation of the standardized fishing success (orange) and the standardized number of sperm whales (green). Lighter thin lines represent variability between captains (see Table2-2). ....................................................................................... 61 Figure 2-9 -Probability to leave a ground, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number of killer whales (green), (Table2-2). The dashed line represents non-significant relationship (see Table2-2). ........................................................................................................ 61 Figure 2-10 -Distributions of the weight of toothfish caught per day for every session, both at Crozet (a) and Kerguelen (b), followed both by a decision to stay (light grey) and by a decision to leave (black), with smoothing splines (coefficient=0.5). On the top we represented the global distributions and on the bottom we showed a zoom of the first values, allowing us to assess the threshold of the fishing success determining the captains' decision to stay or leave. ......................................................................................................................................................... 62 Figure 2-11 -Histograms of the optimal distance score (Equation 2-11) for all captains both at Crozet and Kerguelen. ............................................................................................................................................................. 71 Figure 2-12-Boxplot of the optimal distance score per odontocete occurrence between Crozet and Kerguelen with mean scores represented by the coloured triangles and captains' variability by the grey dots. ........................... 73

Figure 4 - 1 -Figure 4 - 2 -Figure 4 - 3 -

 414243 Figure 2-13 -Boxplot of the extra distance covered after a longline when captains do not chose the optimal decision (i.e. optimal distance score<1) per cetacean occurrence at Crozet, with mean values represented by the coloured triangles. ................................................................................................................................................ 75 Figure 3-1-Scheme of the acoustic array designed by RTSYS deployed during the first mission (2016-2017). .. 82 Figure 3-2 -Picture of the attachment of the SoundTrap on the downline (green rope). The SoundTrap is set permanently on a small rope (the blue one) to facilitate the deployment on the downline. .................................. 83 Figure 3-3 -Position of the recorder during deployment, that is attached to a piece of downline under the buoy. .............................................................................................................................................................................. 85 Figure 3-4 -Spectrogram of sperm whales' clicks (broadband frequencies produced during a very short duration) and killer stereotyped calls (e.g. at 7 s and 9 s). The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). The recording was made during the first mission, the 27 th of in February 2017 on the vessel Ile de La Réunion at Crozet. .............................................................. 87 Figure 3-5 -Spectrograms (left panels) and power spectrum densities (right panels) of 2 French (vessel #1 andvessel #2) and 1 Australian (vessel #3) longliners (one vessel per line). The spectrograms were made over 1 min for vessels at 1 km for the hydrophone, with a colour scale representing the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). The power spectrum (in dB re 1 µPa) were assessed through PAM guide with batched recording files of 3 min for vessels at 1 km of the hydrophone[START_REF] Merchant | Measuring acoustic habitats[END_REF]. See Supplementary data for the 3 other vessels. ............................................................................... 91 Figure 3-6 -Spectrogram of the boat ending its setting (until 38 s) and moving forward (defined as 'post-setting'). The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). Clicks of sperm whales are also present (broadband frequencies produced during a very short duration). .............................................................................................................................................................. 95 Figure 3-7-Spectrogram of the boat hauling a longline with two backward manoeuvres at 8 s and 21 s of the recording. The colour scale represents the square modulus of the acoustic pressure in units of power spectral density (in dB re 1 µPa².Hz -1 ). .............................................................................................................................. 96 Figure 4-1 -Distribution of d (Equation 4-1) centred on 0: dcentred(t)= d(t)-mean(d)......................................... 106 Figure 4-2 -Example of prey catch attempts estimations, in red, over a dive profile with buzzes events, in green (top panel). Value of d(t) over the dive, with the over-dispersed values considered as PCA in red (middle panel). Distribution of d, with the threshold (|min(d)|) estimation represented by the red line (bottom panel). ............ 109 Figure 4-3 -Regression between the number of buzzes and the number of prey catch attempts estimated by our method. The mixed linear model equation (black line) was: Bij=0.46x Pij +(2.9±6.2). ...................................... 110 Figure 4-4 -Estimation of the number of buzzes (green) for each individual from the linear regression (dark line) estimated by the 5 other individuals (blue). Equation for each regression with the slope's p-value were noticed on each graph. The numbers of PCA of each individual not considered in the regression were represented in red. The estimated number of buzzes for each individual (green) should follow the equation y=x (grey line) for a correct estimation. ........................................................................................................................................................... 111 Figure 4-5 -Histograms of association (%) between buzz and PCA for the 6 individuals' dives. ..................... 113 Figure 4-6 -Map of the study area with the locations of the fishing activity (orange dots) around Crozet and Kerguelen. The green rectangle indicates the seamount where the deployment of instruments on killer whales occurred. ............................................................................................................................................................. 120 Figure 4-7 -ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel). The ARGOS position is indicated on the map by the red circle with the diameter representing the location estimate error buffer (cf. CLS classes). The color shade depicted the bathymetry. The soaking longlines are shown in black and the longline being hauled is shown in red at the given transmission time of the ARGOS position (left panel). The dive profile assessed the depth range estimated by the tag through the thickness of the drawing (right panel). On the dive profile the transmission time of the ARGOS position is represented by the red triangle and the bathymetry recorded by the boat during the hauling session is indicated by the red line, as the killer whale was interacting with the boat the red line represented then the bathymetry below the individual for a given time (right panel). ................................................................................................................................................................. 124 Figure 4-8 -ARGOS positions (left panel) of an 'interaction before hauling' with their associate dive profile (right panel) within a 15 hour-window. The ARGOS positions are indicated on the map by the red circles (numbered chronologically along track) with the diameter representing the location estimate error buffer (cf. CLS classes).

Figure 2 :

 2 Figure 2 : Fenêtre de contrôle pour le déploiement

Figure 3 :

 3 Figure 3 : système permanent d'accroche du soundtrap

Figure 4 :☐

 4 Figure 4 : insertion du système d'accroche dans l'orin.

Table 1 -

 1 1 -Summary of the field work conducted within this thesis.

	Mission	Vessel host	Duration	Field work
	First mission	Mascareignes 3	53 days
	2016-2017	(SAPMER)	08/12/16 -30/01/17	-Acoustic array
		Ile De La Reunion	28 days	-Loggers on longlines
		(COMATA)	03/02/17 -03/03/17	-Biopsy sampling
		Albius	11 days	-Photo-identification
		(SAPMER)	03/03/17 -14/03/17
	Second mission 2017-2018	Ile De La Reunion (COMATA)	35 days 14/12/17 -18/01/18	-Acoustic array -Loggers on longlines -Odontocete tagging
		Mascareignes 3	48 days	-Protection systems
		(SAPMER)	18/01/18 -07/03/18	-Biopsy sampling
				-Photo-identification
				-Fishery observer
				(1 month on MAS 3)

J. Fish. Aquat. Sci. 75: 964-976 (2018) dx.doi.org/10.1139/cjfas-2016-0498 Gaëtan Richard 1,2,3* , Christophe Guinet 1 , Julien Bonnel 2 , Nicolas Gasco 4 and Paul Tixier 1,3

published in : Can.

1

Centre d'Etudes Biologiques de Chizé, UMR 7273 -CNRS & Université de La Rochelle, 79360 Villiers-en-Bois, France 2 Lab-STICC UMR 6285, ENSTA Bretagne, 2 rue François Verny, 29806 Brest Cedex 9, France 3 School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia 4 Museum National d'Histoire Naturelle, UMR 7208 BOREA, CP 26, 43 rue Cuvier, 75005 Paris, France

Table 2 -

 2 1 -Results of the GLMMs of the probability to leave for a captain in relation to the fishing success and the competition, with "absence" as the baseline, and random effects were set between captains (intercept) and on the daily weight of toothfish caught (slope).

		Parameter	Value	SE	z-value	p-value
		Intercept	-1.26	0.24	-5.17	<0.001
	CROZET
	N=1241	Fishing success	-1.18	0.18	-6.49	<0.001
	Random effects:
	Intercept's variance=0.33	Presence of both species	0.70	0.22	3.16	0.002
	Slope's variance=0.23	Presence of killer whales only	0.30	0.29	1.03	0.30
		Presence of sperm whales only	0.47	0.22	2.10	0.04
	KERGUELEN	Intercept	-2.19	0.17	-13.24	<0.001
	N=4277
	Random effects:	Fishing success	-1.20	0.15	-8.00	<0.001
	Intercept's variance=0.36
	Slope's variance=0.31	Presence of sperm whales only	0.51	0.08	6.06	<0.001

Table 2 -

 2 2 -Results of the GLMMs of the probability to leave in relation to daily weight of toothfish caught (fishing success) and the number of individuals for the different cetaceans' occurrence possible(both species, killer whales alone and sperm whales alone), with random effects set between the captains and in some models on the slope (according to the model selection based upon the AIC).

	Localisation	Occurrence	Parameter	Value	SE	z-value	p-value
		Cetaceans	Intercept	-0.64	0.20	-3.24	0.001
		N=790
		Random effects:	Fishing success	-1.21	0.25	-4.85	<0.001
		Intercept's variance=0.42
		Fishing success	Number of cetaceans	0.21	0.09	2.27	0.02
		variance=0.56
			Interaction	-0.25	0.11	-2.39	0.02
			fishing success x nb
			cetaceans
		Killer whales	Intercept	-1.04	0.29	-3.62	<0.001
	CROZET	N=78
		Random effects:	Fishing success	-0.80	0.36	-2.21	0.03
		Intercept's variance=0
			Number of killer whales	0.46	0.26	1.75	0.08
		Sperm whales	Intercept	-0.77	0.16	-4.68	<0.001
		N=457
		Random effects:	Fishing success	-0.64	0.13	-4.92	<0.001
		Intercept's variance=0.11
		Nb ind variance=0.17	Number of sperm whales	0.54	0.18	2.99	0.003
		Sperm whales N=3126	Intercept	-1.78	0.14	-12.58	<0.001
	KERGUELEN	Random effects:	Fishing success	-1.07	0.14	-7.44	<0.001
		Intercept's variance=0.24
		Fishing success	Number of sperm whales	0.40	0.05	8.78	<0.001
		variance=0.23

Table 2

 2

-2)

. The dashed line represents non-significant relationship (see

.

Table 3 -

 3 1 -Summary of the dataset acquired during this PhD from personal mission and through the deployments by fishermen and fishery controlers on French and Australina longliners. The Australian fishermen record 15 min every 30 min, so the recording durations are not equivalent to the deployments durations (twice higher than values in the table).

	Operator	Fishery	Vessels	Number of trips covered	Number of deployments	Total recording duration (hours)
			Mission 2016-2017
	Personal contribution	French	(Mascareignes 3, Ile De La Réunion, Albius)	1	17	656
			Mission 2017-2018	1	26	882
		French	(Ile De La Réunion,
			Mascareignes 3)
		French	Albius	2	14	652
		French	Cap Horn	1	6	189
		French	Cap Kersaint	1	7	246
		French	Ile De La Réunion	2	18	737
		French	Ile Bourbon	1	20	695
	Fishermen and
	fishery	French	Mascareignes 3	1	2	72
	observers
		French	Saint André	2	22	469
		Australian	Antarctic Chieftain	1	15	332
		Australian	Atlas Cove	1	18	405
		Australian	Corinthian Bay	2	17	360
		Australian	Isla Eden	2	21	390
		TOTAL		18	203	6085

Table 3 -

 3 2 -Summary of sounds levels and propagation range estimations for the vessels acoustic whilegoing forward or manoeuvring backward.

		RL		Propagation range	Propagation range
	Frequency	(dB re 1 µPa².Hz -1 )	estimations (km) Forward	estimations (km) Backward
	(Hz)
		Forward	Backward	[Min; Max]	[Min; Max]
	250	78.2	89.6	[7 ; 35]	[30 ; 270]
	1000	74.1	82.9	[5 ; 15]	[10 ; 55]
	2750	60.3	75.6	[0 ; 2]	[5 ; 20]

Table 4 -

 4 1 -Summary of the models' AIC.

Table 4 -

 4 2 -Sumamry of the error estimation of the buzzes numbers per dive per individuals (Figure.

	ID	Mean accurate number of	Mean estimated	Mean error	% of error
		buzzes per dive	number of buzzes	number of
			per dive	buzzes
	1	37	35.3±6.2	-1.7±3.8	-3.7±9.5%
	2	33.8	24.1±6.2	-9.7±6.6	-26.5±12.7%
	3	19.4	22.6±6.2	3.2±2.3	17.6±13.2%
	4	17.8	20.1±6.2	2.3±3.8	14.9±22.4%
	5	25.6	22.5±6.2	-3.1±2.8	-11.7±9.8%
	6	9.3	18.2±6.2	8.9±0.9	102.8±34.5%

1,2,3 , Julien Bonnel 4 , Paul Tixier 3 , John P.Y. Arnould 3 , Anaïs Janc 1 , Christophe Guinet 1 1

  Centre d'Études Biologiques de Chizé, UMR 7372 -CNRS & ULR, 79360 Villiers-en-Bois, France 2 Lab-STICC UMR 6285, ENSTA Bretagne, 2 rue François Verny, 29806 Brest Cedex 9, France

3

School of Life and Environmental Sciences (Burwood campus), Deakin University, Australia

4

Woods Hole Oceanographic Institution, 266 Woods Hole Rd, Woods Hole, MA 02543-1050, USA

  Supp. data 3 -AICs of the GLMMs of the probability to leave in relation to the fishing success and the number of individuals for both Crozet and Kerguelen. Best AIC are in bold.

					Crozet			Kerguelen
				df	Crozet	AIC		df	AIC Kerguelen
	Null Models	Cetaceans	4	1532 Killer whales	4 Sperm whales	Sperm whales
		df	AIC	df	AIC	df	AIC	df	AIC
	Fishing success * competition Fishing success * nb individuals			11		1501		7
	random= Fishing success |captain random= Fishing success * nb ind |captain	14	965	14	115	14	575	14
	Fishing success * nb individuals
	Fishing success * competition random= Fishing success + nb ind |captain	10	9 956	10	1510 103	10	5 566	10
	random=1|captain
	Daily weight of toothfish * nb individuals			8		1499		6
	Fishing success + competition random= nb individuals |captain	7	969	7	99	7	562	7
	random= Fishing success |captain
	Fishing success * nb individuals
	random= Fishing success |captain	7	952	7	97	7	566	7
	Null			4		1532		4
	Fishing success * nb individuals
	random=1|captain	5	969	5	95	5	564	5
	Fishing success + nb individuals random= Fishing success |captain	6	956	-	-	-	-	6
	Fishing success + nb individuals
	random= nb individuals |captain	-	-		-	-	6	561	-	-
	Fishing success + nb individuals
	random=1|captain	-	-		4	93	-	-	-	-
	Null	2	1073	2	99	2	600	2

Table 2 -

 2 [START_REF] Morrissey | Passive acoustic detection a nd localization of sperm whales (Physeter macrocephalus) in the tongue of the ocean[END_REF], showing the probabilities to leave a ground as a function of one of the two standardized variable, and the color's gradients of dashed lines from light to dark represent variation of the other variable from minimum value to maximum value. b) The lightest orange dashed lines represent the probability to leave a ground as a function of fishing success the minimum number of cetaceans (i.e. in absence), and the darkest orange dashed line for the maximum number of cetaceans (i.e. 29 individuals). c) The lightest green dashed lines represent the probability to leave a ground as a function of the number of cetaceans for the minimum fishing success (i.e. a null success), and the darkest green dashed line for the maximum fishing success (i.e. 15650 kg). ............................ 60 Figure2-8 -Probabilities to leave a ground, π, at Crozet (a) and atKerguelen (b) in response to the variation of the standardized fishing success (orange) and the standardized number of sperm whales (green). Lighter thin lines represent variability between captains (see Table2-2). ....................................................................................... 61 Figure 2-9 -Probability to leave a ground, π, at Crozet in response to the variation of the standardized fishing success (orange) and the standardized number of killer whales (green), (Table2-2). The dashed line represents non-significant relationship (see Table2

COMMENTAIRES Récupération Officier au virage Rang du virage lors de la remontée de l'hydrophone Date et heure remontée Soundtrap Condition océano

	Soundtrap
	____
			Mouillage
	Fiche de déploiement	Numéro de la palangre
	NB : enregistrer le fichier avec le code	Profondeur Enregistreur
	A_XXX(num manip)_ZEE_CAP_NAV_AAMMJJ(déployé)_PALXXX
	Présence cétacé	☐ abs	☐ orques	☐ cachalots	☐ les 2	☐ non obs
		Etat de la mer :
		Taille vague :
		Force du vent :
		Direction du vent :
		Météo :
		Beaufort (0-12) :
	Présence cétacé	☐ abs	☐ orques	☐ cachalots	☐ les 2	☐ non obs
	COMMENTAIRES
	Evènements acoustiques
	Date dd/mm/yy				Evènement
	-heure hh :mm	Position GPS	Croche Pal XXX/ Demi-tour/ marche arrière/
	(remettre la date que si elle change)			autre manoeuvre/ cétacés (autre que orque et cachalots)

Supp. data 5 -Welch's power spectral densities of the forward, backward and ambient noise estimated with PAM Guide.

Supp. data 9 -Picture of an entangled dead sperm whale (by Gabriel DEVIQUE, fishery controller).

Supp. data 10-ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel).Supp. data 11-ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel).

Supp. data 12-ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel).Supp. data 13-ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel).

De la part de tous les acteurs du projet OrcaDepred, nous vous remercions, ainsi que les capitaines et leur équipage, de votre aide à la bonne réalisation de cette étude, contribuant à une meilleure compréhension de ce phénomène de déprédation.

Remerciements

ACKNOWLEDGMENTS

SUPPLEMENTARY DATA

Supp. data 1 -Summary of the number of sessions in both EEZs with decision to stay or leave, in absence of presence of cetaceans (each species only or both simultaneously). Supp. data 14-ARGOS position (left panel) of an 'interaction during hauling' with its associate dive profile (right panel).

Supp data 15-ARGOS positions (left panel) within a 3 hour-window of 'no interaction' with the associate dive profile (right panel). Passive acoustic monitoring is now intensively used to study cetaceans. Given the growing amount of collected data, it is of interest to implement automatic methods to monitor cetaceans' sounds. In that aim, we developed DeteClic, a transparent a nd user-friendly odontocetes click detector on Matlab. This detector takes the form of an intuitive interface to analyze pulsed and loud acoustic signals. DeteClic can be used along the entire bioacoustics analyzing process, from the display of graphic representations to the click detection and their understanding. Our detector allows for (i) manual labeling, based on both listening and visualization, and (ii) automatic detection. DeteClic includes 4 different automatic click detection approaches: (i) a Teager-Kaiser energy operator [START_REF] Madhusudhana | Automatic detection of echolocation clicks based on a Gabor model of their waveform[END_REF], (ii) an intercorrelation computation with a given reference signal, (iii) a spectrogram analysis [START_REF] Morrissey | Passive acoustic detection a nd localization of sperm whales (Physeter macrocephalus) in the tongue of the ocean[END_REF], and (iv) a kurtosis-based statistical detection [START_REF] Gervaise | Automatic detection of bioacoustics impulses based on kurtosis under weak signal to noise ratio[END_REF].

LIST OF TABLES

Combining the results of these 4 methods enhances the automatic detection robustness. To assess its performances, we equipped DeteClic with an evaluation tool, relying on automatic comparison of manually-and automatically-detected clicks. DeteClic can therefore be used to compare the results of the methods (i.e. recall and precision rates [START_REF] Gillespie | Automatic detection and classification of odontocete whistles[END_REF]) depending on acoustic environments and recordings. In addition to the clicks detection and based on how the user defines a train of clicks (using criteria in time between clicks and train duration [START_REF] Whitehead | Click rates from sperm whales[END_REF]), DeteClic enables a presence event analysis. Finally, we provide a series of outputs easily reusable for any further statistical or visual analysis. We first tested DeteClic on sperm whale clicks, recorded during longline fishing campaigns around the Kerguelen Islands (southern Indian Ocean). Considering clicks detected by at least three methods was the most efficient approach (average recall of 71±5% and precision of 54±5%). Discriminating click trains allowed for a greater precision in detecting echolocation events and an accurate presence event analysis.

APPENDIX 3 -HYDROPHONE

Pourquoi récolter des données acoustiques ?

Le projet ORCADEPRED mené par Dr. Christophe GUINET (CEBC, UMR CNRS -Université La Rochelle) vise à mieux comprendre le phénomène de déprédation réalisé par deux espèces de cétacés (les orques et les cachalots) sur la pêcherie à la légine dans les ZEE de Crozet et Kerguelen. Ces interactions entraînent de nombreux problèmes, aussi bien socio-économiques qu'écologiques. L'objectif à long terme est donc de trouver des solutions pour réduire voire empêcher la déprédation.

Jusqu'à présent les études du projet ont été réalisées sur les données de pêche et les observations de surface réalisées par les COPECs. Ces informations ont permis d'apporter de nombreuses réponses sur la quantification du phénomène et du suivi des populations de cétacés. Cependant une dimension reste mal connue : le comportement sub-surface. Or il est difficile « d'observer » ce qu'on ne peut voir, c'est pour cela que le projet a mis en place une étude acoustique. Le but est donc d'équiper les palangres avec des hydrophones.

Les orques et cachalots sont des mammifères qui produisent de nombreux sons, que ça soit pour communiquer ou pour chasser. En effet ces animaux évoluent dans un milieu où la lumière est rare et donc ils utilisent l'écholocation (principe du sonar) pour rechercher leur nourriture. En déployant des hydrophones (i.e. des microphones sousmarins) sur les palangres, il est possible de surveiller la présence des cétacés autour des lignes même en absence du navire. De plus, comme indiqué précédemment, l'acoustique est très importante pour ces mammifères marins, c'est donc essentiellement de cette manière qu'ils repèrent les navires. Il est donc important d'enregistrer les palangriers pour quantifier et qualifier leur signature acoustique et d'évaluer si les différents bruits générés lors des opérations de pêches permettent une détection, et à quelle distance, des navires de pêche par les cétacés.

Description de l'enregistreur

Accessoires fournies avec le Soundtrap :

Configuration de l'enregistreur

La configuration nécessite le logiciel SoundTrap Host présent sur votre disque dur fourni.

Installation de SoundTrap Host sur votre PC Ne pas brancher le Soundtrap sur votre ordinateur. Lancer l'exécutable : « SoundTrapHostinstaller».

Une fois installé, lancez le logiciel SoundTrap Host.

Connecter le SoundTrap au PC

Connecter le Soundtrap à l'ordinateur avec le câble USB fourni, l'appareil va alors apparaître dans la liste du logiciel après quelques minutes (Figure 1). Le sélectionner en cliquant dessus.

Control du statut du Soundtrap

Vous pouvez vérifier l'état de la batterie, de la mémoire ou encore de l'heure de l'appareil (synchronisé à celle de l'ordinateur) dans l'onglet 'Status' (Figure 2). Avant un déploiement faites en sorte que la mémoire soit vide et la batterie pleine.

Recharge de la batterie

La batteire du Soundtrap est une batterie Lithium-ion rechargeable. La recharge s'effectue avec le cable USB soit sur un ordinateur soit sur une prise acceptant le port USB. Une LED bleue s'allume lorsque le Soundtrap recharge. Il faut environ 8h pour recharger entièrement une batterie vide, et la charge s'effectue plus rapidement lorsque le Soundtrap n'est pas connecté au logiciel SoundTrap Host. Lorsque la batterie est rechargée, la LED s'éteint.

Déploiement et récupération de l'enregistreur sur une palangre Démarrer et arrêter le Soundtrap

Les palangres étant déployés de nuit, vous ne serez pas présent, donc soit vous demandez aux marins de démarrer l'hydrophone avec la télécommande fournie, soit vous programmez le début d'enregistrement à l'heure prévu de la mise à l'eau par le capitaine. Il est préférable de mettre l'hydrophone sur une palangre qui sera remontée en dernière d'une zone ou d'une session de virage. Il est du plus grand intérêt d'avoir l'hydrophone le plus longtemps à l'eau afin de pouvoir enregistrer le plus de virages possible (moment où il y a déprédation).

Au niveau de la palangre, il est préférable de mettre l'hydrophone sous la bouée qui sera remontée en second, afin d'enregistrer le virage de la palangre équipée. Cependant, si la bouée avec l'hydrophone peut être quelque fois remontée en premier cela est aussi intéressant pour enregistrer l'approche du navire. Ainsi essayez d'avoir au moins 3 enregistrements avec l'hydrophone remonté avec la première bouée de la palangre équipée.

Téléchargement des données de l'enregistreur