Mathilde Huon
email: mathilde.huon@univ-lr.fr

Esther L Jones

Bernie J Mcconnell

C Ecile

Yann Planque

Florence Caurant

David Pinaud

Cécile Vincent
email: cvincent@univ-lr.fr

Willy Dabin

Armel Deniau

Stéphane Dixneuf

Laetitia Dupuis

Jean-François Elder

Marie-Hélène Fremau

Sami Hassani

Audrey Hemon

Jacky Karpouzopoulos

Cécile Lefeuvre

Simon E W Moss

Pascal Provost

Jérôme Spitz

Yannis Turpin

Vincent Ridoux

Habitat Selection of Gray Seals (Halichoerus grypus) in a Marine Protected Area in France

Keywords: conservation, GLM, gray seal, habitat preference, Iroise Sea, management, spatial planning, telemetry

Je tiens tout d'abord à remercier mes directrices de thèse Florence Caurant et Cécile Vincent pour m'avoir encadrée et soutenue pendant ces 4 années. Cécile, merci de m'avoir permis d'entrer dans le monde de la télémétrie il y a de ça presque 7 ans. Merci au staff du SMRU, en particuliers à Bernie McConnell et Phil Hammond pour m'avoir accueillie au sein de leur équipe lors de mes différents séjours à Saint Andrews. Merci à eux deux ainsi qu'à Debbie Russell, Esther Jones ainsi que Mick Wu pour leur aide et leurs conseils avisés. Je tiens également à remercier Clint Blight pour m'avoir hébergée pendant ces séjours et pour les randonnées dans les montagnes Ecossaises

Inter and intra specific interactions in marine habitat selection of two sympatric seal species in the Northeast Atlantic

Thèse présentée par Mathilde HUON

Dessin: Michel Salaün -Océanopolis

Merci à mes amis et collègues du rochelais … Anne, Pierre, Anouk, Bastien, Cyril, Yann, Thomas, Guillaume… pour les moments de détente au labo, mais surtout en dehors J Un grand merci à Guillaume, Anouk, Yann, Cyril, Hien, Eva, Adrian pour m'avoir soutenue (et nourrie accessoirement) dans cette dernière ligne droite. Anass merci à toi pour ton thé à la menthe et tes petits déjeunés marocains.

Vanessa, un grand merci d'être toujours là malgré la distance.

Je souhaite terminer par remercier ma famille. JY pour son soutien inconditionnel et sa patience à toutes épreuves. Anne et Patrick, merci infiniment d'avoir toujours été là.

François, sans qui je n'aurai jamais eu vent de cette source de financements. Merci à vous pour votre soutien dans l'accomplissement de mes projets et pour m'aider à repousser les limites, en particulier avec l'ascension du Djebel Toubkal. Regarding the analyses carried out during this PhD and presented in this thesis, I conducted all the analyses on the foraging habitat modelling at the colony scale for grey and harbour seals, and also on spatial usage (Chapters 3, 4 and 5). The analyses of grey seals habitat modelling at individual scale were carried out by Marie Windstein for her Master Thesis, under my co-supervision.

Other publications are presented in Annexes ANNEXE I: Habitat selection of gray seals (Halichoerus grypus) in a marine protected area in France. This publication included results from my Master Thesis, obtained from the analyse of habitat modelling and wrote the publication.

ANNEXE III: Grey and harbour seals in France: Distribution at-sea, connectivity and trends in abundance at haulout site. For this publication I was involved in the statistical analyses and the revision of the manuscript.

ANNEXE IV: The importance of locations in shapping habitat selection and effect of behavioural plasticity in a top marine predator. This manuscript was written during my stay at the UCC. I carried out all the analyses. This is a preliminary draft and the title has to change, as I aim at adding the results from the Scottish and French study areas (corresponding to the chapter 4).

Publications -Conferences -Training courses

• Publications Title Status Section Huon, M., Jones, E., Matthiopoulos, J., McConnell, B., Caurant, F., Vincent C. (2015). Habitat selection of gray seals (Halichoerus grypus) in a marine protected area in France. Journal of Wildlife Management Nowadays, "Habitat" can be perceived as an ambiguous term in ecology. This term is often used to represent biome, ecosystem, community, spatial mosaic or foraging patch [START_REF] Morris | Ecological Scale and Habitat Use[END_REF]. Thus different definitions of habitat exist in the literature. [START_REF] Krebs | Behavioural Ecology: an evolutionary approach[END_REF] defined habitat as a "particular environment in which species live, or broadly speaking the biotic environment occupied by an animal". [START_REF] Tirard | Ecologie[END_REF] defined it as a geographical place presenting some features allowing a population to be viable over a long time. This term was defined as "spatially bounded subsets of abiotic and biotic conditions among which population density of local species varies from other adjacent subsets" by [START_REF] Morris | Ecological Scale and Habitat Use[END_REF]. The term habitat is often mixed up with two other different terms:

"environment" and "ecological niche". The environment can be defined as "all the biotic and abiotic factors that actually affect any individual organism at any points in its life cycle", [START_REF] Krebs | Behavioural Ecology: an evolutionary approach[END_REF]. The ecological niche can be defined as the species (or individual) role in the ecosystem functioning. [START_REF] Odum | Fundamentals of ecology[END_REF] a pioneer in ecology, said "The ecological niche is the species' profession (prey, predator, parasite…) and the habitat is the address of this species". One species is linked by three characteristics to its environment: its geographical distribution, its habitat, and its ecological niche. Here we defined habitat as a spatially limited space with physical and chemical conditions where different interacting organisms can evolve.

Different types of habitat defined by physical and chemical processes can be identified.

Terrestrial habitat types include forests, grasslands, wetlands and deserts; freshwaters habitats include river, streams, lake, ponds, marshes and bogs; and marine habitats include open sea, estuaries, bays, intertidal areas, sea bed, reefs etc.

Habitat patches and mobility

One habitat is composed by a mosaic of different small habitats (or patches) in which organisms can evolve. These small habitats are differently exposed to light, temperature, humidity, and air factor. Due to the difference in their abiotic and biotic conditions, mobile organisms move between these different small habitats to realise their life cycle, some of them being more suitable for some key periods of this life cycle (resting, moulting, breeding, reproduction, foraging). This is especially true as these small habitats are in constant spatial and temporal evolution, representing dynamic habitats. We can take seasonal variation as an example. One habitat can represent good ecological conditions to a species during the summer (good air temperature, light, food availability), versus poor conditions during winter (i.e. air temperature too cold, too much darkness, low food availability). This motivates mobile organisms to migrate from one habitat to another. The migration can occur at different scales, depending on the mobility capacity of the organism.

In the case of humpback whales (Megaptera novaeangliae) several thousand kilometres of distance separate the two main habitats [START_REF] Stevick | Migrations of individually identified humpback whales between the Antarctic Peninsula and South America[END_REF]. They move to the Antarctic during Northern hemisphere winter in order to feed on high densities of krill and make blubber reserves for the next six months, when they move back to tropical area during spring and summer to give birth and breed. In this latter area, the abiotic conditions are better for their calves, while adults fast due to the lack of krill [START_REF] Rasmussen | Southern Hemisphere humpback whales wintering off Central America: insights from water temperature into the longest mammalian migration[END_REF]. The Bristish Pied Waigtail (Motacilla alba yarrellii) breeds in England during spring and summer, and migrates to suitable foraging habitat during the winter: from France to Portugal, several hundreds of kilometres from their breeding grounds [START_REF] Davis | The movements of Pied Wagtails as shown by ringing[END_REF]. This mobility represents an adaptation to the habitat variability and is a source of speciation. Contrary to these mobile organisms, others, with a low mobility do not have the capacity to move between habitats when the climatic conditions change. In that case, they can enter in hibernation as the wood turtle (Clemmys insculpta, [START_REF] Compton | Habitat selection by wood turtles (Clemmys insculpta): an application of paired logistic regression[END_REF].

Thus, as animals evolve in dynamic habitats with a certain degree of mobility, these habitats can be perceived at different temporal and spatial scales linked together [START_REF] Bissonette | The Influence of Spatial Scale and Scale-Sensitive Properties on Habitat Selection by American Marten[END_REF][START_REF] Wiens | Spatial Scaling in Ecology[END_REF]. The figure 1.2-1 represents the link between spatial and temporal scales. Depending on the temporal scale (represented by the x axis) or the spatial scale (represented by the Y axis), we are looking at different levels, from the individual to the population or species. At fine temporal (hours or days) and spatial scale, we can focus on the use of patches (or micro-habitats) by an individual. If we look at the highest scale (decades), this gives an overview of the global habitat dynamic at a regional or even larger spatial scale where persistent populations or species occur. Animals often use different components of the habitat at different scales, and populations or species vary in their scale of habitat usage. [START_REF] Morris | Toward an Ecological Synthesis: A Case for Habitat Selection[END_REF] defined and described the different scale terminologies. For this author, the ecologists defined the smallest level of spatial and temporal resolution as grain, the largest area of duration of investigation as extent, the ratio of extent to grain as scope, defining as the same time the micro-scale and macro-scale.

Adaptation to habitat dynamic -Behavioural plasticity

The biotic and abiotic characteristics of the environment vary in time, and the animals need to adapt to these temporal and spatial dynamics. Indeed, environmental variability is one of the factors that most strongly affect fitness (i.e. survival and reproductive success, [START_REF] Caughley | Wildlife ecology and management[END_REF][START_REF] Lande | Risk of Population Extinction from Fixation of New Deleterious Mutations[END_REF][START_REF] Primarck | Essentials of conservation biology[END_REF]. Resource availability, predator pressure, or temperature may influence population growth by affecting individual size, juvenile or adult mortality, and reproduction rate [START_REF] Ahnesjo | Temperature affects male and female potential reproductive rates differently in the sex-role reversed pipefish, Syngnathus typhle[END_REF][START_REF] Reznick | The Impact of Predation on Life History Evolution in Trinidadian Guppies (Poecilia reticulata)[END_REF][START_REF] Reznick | The Influence of Fluctuating Resources on Life History: Patterns of Allocation and Plasticity in Female Guppies[END_REF][START_REF] Saether | The adaptive significance of reproductive strategies in ungulates[END_REF][START_REF] Smith | Phenotypic Design, Plasticity, and Ecological Performance in Two Tadpole Species[END_REF] Organisms may adapt their behaviour to overcome these pressures. This behaviour is linked to the phenotypic plasticity. We can define the phenotypic plasticity as the capacity of an organism to express the different phenotypes from one specific genotype according to environmental conditions (Komers, 1997). There is growing evidence that organisms can respond to environmental changes by producing adaptive phenotypes, and that the degree of plasticity is under evolutionary pressure [START_REF] Stearns | The Evolutionary Significance of Phenotypic Plasticity[END_REF]. This degree of plasticity is influenced by the interaction between heritable factors, social environment, organism status such as age, sex, etc. This phenotypic plasticity will give the organism different possibilities to deal with these pressures by adapting their behaviour, defining the term "behavioural plasticity". The amplitude of the behavioural answer will depend on the range of biotic and abiotic pressures in the habitat. Two different forms of behavioural plasticity exist, the innate response and the one linked to the adjustment through learning (this latter representing the most important, according to (Komers, 1997). The behavioural plasticity developed by learning is based on its modification by experience [START_REF] Stephens | Change, regularity, and value in the evolution of animal learning[END_REF], resulting in behavioural plasticity including immediate and reversible responses. The evolution of learning may be constrained by factors that are extrinsic and/or intrinsic to the animal.

Animals are able to adjust their behavioural plasticity as a trade off between cost and benefits [START_REF] Parker | behavioural ecology: an evolutionary approach, Blackwell scientific[END_REF]. Furthermore, because this trade-off varies with age, the behavioural plasticity will change (i.e. in general juveniles employ more diverse behavioural strategies than adults). The costs of plasticity can reduce different aspects of the life such as reproduction or growth [START_REF] Newman | Adaptive Plasticity in Development of Scaphiopus couchii Tadpoles in Desert Ponds[END_REF][START_REF] Parejko | The Evolutionary Ecology of an Antipredator Reaction Norm: Daphnia Pulex and Chaoborus Americanus[END_REF][START_REF] Rodd | Complex origins of variation in the sexual behaviour of male Trinidadian guppies,Poecilia reticulata: interactions between social environment, heredity, body size and age[END_REF][START_REF] Spitze | Predator-Mediated Plasticity of Prey Life History and Morphology: Chaoborus americanus Predation on Daphnia pulex[END_REF].

The behavioural plasticity of an organism can change at its range limit. The organism's tolerance to the biotic and abiotic pressures determines this range's limit. Indeed, these pressures can become discordant with organism's biology, inducing restricted habitat use. Individuals living at the range's limit present local adaptation compared to individuals living in the core distribution area thus increase their behavioural plasticity (Komers, 1997;[START_REF] Sexton | Evolution and Ecology of Species Range Limits[END_REF]. These individuals might exhibit a greater adaptation to environmental variability (i.e. when environmental fluctuations exceed the species' tolerance).

Organism interactions

Mobile organisms that move from one patch to another meet and interact with other individuals. Interactions can occur between two or more individuals of the same species (belonging to a population) or between different species defining respectively intra and interspecific interactions. There are different kinds of interactions depending on the nature of their consequences that can be positive, negative or neutral.

• Mutual interactions between two species imply that both species can benefit from these interactions.

• Competition occurs between organisms for the access to limited resources or space in the habitat [START_REF] Tirard | Ecologie[END_REF]. It can occur within species (intra-specific competition) and between species (inter-specific competition). Inter-specific competition mainly occurs between species with similar ecological niche (i.e. sympatric species). The size of the population (of one or the two species) may have an impact on the prey depletion in the patches including prey resources, creating a density-dependence phenomenon. Indeed, prey resources are not present at libitum in the different patches, so the number of patches visited to find prey will increase (increasing total distance travelled from the starting point). This competition between individuals of the same species or sympatric species produces the Niche Divergence Hypothesis [START_REF] Hughes | Optimal foraging theory in the marine context[END_REF][START_REF] Schoener | Models of Optimal Size for Solitary Predators[END_REF]. This concept represents the spatial and temporal segregation among individuals or species. In many species, according to their age, sex and morphology, individuals exhibit significant variations in foraging behaviour and diet specialization, thus defining the population niche width. The resource partitioning within species can be indicated by differences of morphology or body size among individuals, which are connected to specific foraging strategies and abilities. [START_REF] Field | Resource Partitioning through Oceanic Segregation of Foraging Juvenile Southern Elephant Seals (Mirounga leonina)[END_REF] studied the resource partitioning in southern elephant seal (Mirounga leonina, intra-specific competition). As juveniles were physiologically and morphologically limited compared to the adults, they used smaller areas, making more and shorter trips to sea than adults. Resource partitioning was also highlighted in two sympatric species, the fur seals (Arctocephalus galapagoensis) and sea lions (Zalophus californianus wollebacki) in the Galapagos [START_REF] Dellinger | Fish prey of the sympatric Galapagos Fur Seals and Sea Lions: seasonal variation and niche separation[END_REF]. Despite habitat overlap, these two species were temporally segregated (one foraging during day time and the other one during night time), and due to their morphological differences, they foraged on different prey. Furthermore, to deal with competition, a specialisation of individuals or species on a certain type of prey can occur. Heterogeneous habitat may lower the effect of competition, favouring individual or species specialisation. This kind of partitioning was shown on two other sympatric species of otariids [START_REF] Franco-Trecu | Individual trophic specialisation and niche segregation explain the contrasting population trends of two sympatric otariids[END_REF]. A trophic specialisation and niche segregation between the South American fur seal (Arctocephalus australis) and the South American sea lion (Otaria flavescens) was highlighted, which were respectively composed by generalist individuals (reaching a larger trophic niche) and by specialized individuals (reaching a smaller trophic niche).

• Different levels of trophic interactions occur. Along with competition, predation is another major type of interaction that can influence the organization of communities. Thus the process of predation can have an influence on the choice of patches by both predators and prey; the prey trying to avoid the predator; and the predator trying to find a patch with a high prey density. In grassland habitat, without the presence of wolves, elks mainly used this habitat, however, in the presence of wolves in this habitat, elks moved into a protective cover of wooded habitat for protection [START_REF] Creel | Elk Alter Habitat Selection as an Antipredator Response to Wolves[END_REF].

Optimal foraging theory

The act of predation is linked to the process of foraging. In their general habitat, animals show their foraging behaviour with the choice of specific patches and the prey type they include (Stephens and Krebs, 1986). [START_REF] Charnov | Optimal foraging: The marginal value theorem[END_REF], [START_REF] Macarthur | On Optimal Use of a Patchy Environment[END_REF] described the "Optimal foraging Theory" to explain the individual foraging strategies. In one habitat composed by different patches, the animal determines the optimal strategy to optimise the utilisation of time in the energetic budget versus the time to search, pursuing and capturing the prey. These authors described for an environment where the resources are limited, the set of expected choices for the predator to guarantee a good energy gain compared to the energetic loss. Furthermore they also studied the effect of using patches of different sizes, which includes the travelling time budget between them (Figure 1.5-1). They determined the optimal strategy in the energetic budget of time utilisation for the time to search, pursuing and capturing the prey, that the animal might adopt when it enter into the patch. The "X axis" represents the time spent travelling (on the left) or spent in the prey patch (on the right). The "Y axis" represents the energy gain. The curve (in blue) represents the energy available of a given prey in the patch. The strait line (in red) represents the curve tangent beginning by the start of the travelling time. The abscissa of the intersection point between the curve and the line correspond to the optimal time spent by the animal within the area to gain the maximum of energy. This figure represents the simplest case and was adapted for multiple prey species and also with the presence of competitors, changing consequently the trade-off of cost and benefit of the time spent per patches. Orians and Pearson, (1979) adapted this theory of "optimal foraging" to the " Central place foraging theory". This theory is a set of functional hypotheses and models that concern animals collecting food and returning to a fixed place, called the central place. The central place can represent a habitat or patch/place where the organism can rest, breed, and also escape from the predation (increasing the fitness). This central place is important for organism in the use of other habitat patches to forage. Depending on different parameters (such as prey availability, patch quality, cost of foraging, travelling and resting at this central place), the forager has to move to its foraging areas from the central place. Animals moving from this central place to foraging patches perform specific trips, which can be

TIME IN PATCH TRAVELING TIME

ENERGY tp divided into three steps: the outbound trip, the foraging trip and the return trip. [START_REF] Horn | The Adaptive Significance of Colonial Nesting in the Brewer's Blackbird (Euphagus Cyanocephalus)[END_REF] defined the "optimal central place" as the one which minimizes travelling time, thus becoming the "centre of gravity" of the food distribution, and assuming that the maximization of rate of energy delivery to the central place is the one that minimizes the traveling time.

This "central place foraging theory" was studied experimentally in captivity as well as in the wild. The model of central place foraging with the insect-feeding bird, the whinchat (Saxicola rubetra) was tested experimentally [START_REF] Andersson | Central Place Foraging in the Whinchat, Saxicola Rubetra[END_REF]. The author recorded in a plane and uniform habitat, the time spent searching at different distances from the nest by five pairs of whinchat with and without additional food distributed over the foraging area.

The predictions about the rapid selection of trips including increasing food density highlighted that the whinchat was under strong selection for efficiency. A trade-off between quality of the foraging habitats and their distances from the central place was highlighted for the cinereous vulture (Aegypiu smonachus), [START_REF] Carrete | Application of central-place foraging theory shows the importance of Mediterranean dehesas for the conservation of the cinereous vulture, Aegypius monachus[END_REF]. Although foraging habitats (oak woodland) were located far away from the colony (i.e. central place), habitat food resource appeared to be of sufficient quality to counterbalance the cost of distance travelled. Furthermore, the distance between the central place and foraging patches can change throughout the life cycle [START_REF] Costa | Reproductive and Foraging Energetics of High Latitude Penguins, Albatrosses and Pinnipeds: Implications for Life History Patterns[END_REF]. It was the case for Adelie penguins (Pygoscelis adelia, Ropert- [START_REF] Ropert-Coudert | Patterns of energy acquisition by a central place forager: benefits of alternating short and long foraging trips[END_REF]. During the breeding period, and depending on the allocation of benefits, the distance performed by adults were not the same. Indeed, they performed long trip to feed on prey with high energetic content when the food were allocated to themselves. On the opposite, they made shorter trips to find prey containing lower energy when the food was allocated to their chick.

As central place forager, birds need to come back to their nest with their preys, mainly to feed their chicks. However, this is not the case for mammal thanks to the process of lactation [START_REF] Costa | Foraging energetics and diving behavior of lactating New Zealand sea lions, Phocarctos hookeri[END_REF]. Pinnipeds are usually defined as either capital breeders or income breeders [START_REF] Boyd | State-Dependent Fertility in Pinnipeds: Contrasting Capital and Income Breeders[END_REF]. The capital breeders build their blubber reserves outside the breeding and reproductive periods, allowing them to remain on land during most or all of the breeding and pupping period, while fasting [START_REF] Stephens | Capital breeding and income breeding: their meaning, measurement, and worth[END_REF]. If several phocid species are considered as capital breeders (such as the grey seal, Halichoerus grypus, crabeater seal, Lobodon carcinophaga, and elephant seal (Beck et al., 2003;[START_REF] Pistorius | Adult female survival, population trend, and the implications of early primiparity in a capital breeder, the southern elephant seal (Mirounga leonina)[END_REF], others are nonetheless considered as intermediate cases between capital and income breeders (as Weddell seals, Leptonychotes weddellii, [START_REF] Wheatley | Feast or famine: evidence for mixed capital-income breeding strategies in Weddell seals[END_REF]). Income breeders, on the other hand, continue to forage at sea during the breeding and pupping period: pupping females in particular alternate nursing periods with foraging trips at sea. In Pinnipeds, this strategy is mainly shown by otariid species ( [START_REF] Mcdonald | Dynamic Influence of Maternal and Pup Traits on Maternal Care during Lactation in an Income Breeder, the Antarctic Fur Seal[END_REF][START_REF] Rehberg | Foraging behavior of adult female Steller sea lions during the breeding season in Southeast Alaska[END_REF].

Habitat selection

Depending on the influence of abiotic and biotic factors, interactions between organisms and biological requirements, an animal can choose its habitat (or patches) at different degrees and (Johnson, 1980a) defined the parameters of this habitat choice: the "abundance" of a component as its quantity in the environment; the availability of that component can be perceived as its accessibility to the consumer. This component can be used (i.e. usage) by the consumers in a fixed period of time in a certain quantity. If the component is used disproportionately to its availability, there is a selection of this component by the organism. By selecting their habitat, animals choose a place to live composed by different patches, where they can adapt their search modes depending on the prey availability [START_REF] Charnov | Optimal Foraging: Some Theoretical Explorations[END_REF]. Indeed animals are supposed to select their habitats where their fitness is favoured [START_REF] Orians | Habitat selection : General theory and applications to human behavior[END_REF]. Different patches available for exploitation compose the general habitat chosen by the animal. As the animal selects its habitat depending on its foraging activities and the trade off between cost and benefits associated, this can be perceived as the foraging habitat selection. (Johnson, 1980b) defined different orders of process of selection. The first order of selection can be perceived as the selection of physical or geographical range of a species, and can be defined as the general selection of the species or population range. The second order selection take place at a smaller scale and can be defined by the home range of an individual or a social group within the range (as the colony in a population of pinnipeds for example). This last order of selection is defined here as local selection. All these definitions regarding the habitat and its selection are linked together and summarised Figure 1.6-1. As central place forager, the wolf lives in a global habitat constituted by a mosaic of patches, one representing its central place ("nest") and the others representing the foraging areas. These foraging areas contain contrasted abiotic conditions with different prey availabilities (represented by deer). Furthermore, these foraging areas are located at different distances from its central place and contain other organisms leading to intra (represented by others wolves) or inter-specific competition. With the influence of its behavioural plasticity and the balance between the cost and benefits (i.e. optimal foraging theory) of travelling over long distance and the quantity of energy delivered within each patch (taking into account the interactions with other organisms, the wolf can select a specific foraging patch (i.e. foraging habitat selection).

PhD aims

The objective of this PhD is to study the local foraging habitat selection of two species living in sympatry at different places and subject to different influences:

-Behavioural plasticity: depending on habitat and prey availability, which differ between sites, the habitat selection is not the same between the different sites.

-Intra and interspecific interactions (competition): allowing a niche partitioning and thus habitat selection segregation at spatial and temporal scales.

To answer to this objective, I focused on two seals species: the grey (Halichoerus grypus) and the harbour seals (Phoca vitulina). These species are two marine top predators foraging at sea and hauling out to rest, breed and moult. Grey seal represents a capital breeder, and harbour seal tends to adopt intermediate behaviour between capital and income breeders [START_REF] Stephens | Capital breeding and income breeding: their meaning, measurement, and worth[END_REF]. In Northeast Atlantic these two species live in sympatry and their respective populations are divided into different colonies with different dynamics and located in contrasted habitat. This represents an interesting situation to study the influence of behavioural plasticity at local scale, depending on habitat and prey availability and colony dynamics on the local spatial usage and foraging habitat selection. Several grey and harbour seals were tagged and tracked around different colonies in France, Ireland and Scotland.

The structure of this thesis is articulated as following:

1. CHAPTER 2: Material and Methods. This chapter first describes in detail the characteristics of the different areas in the Atlantic Northeast and the ecology of the two seal species. Secondly, I present the telemetry tags deployed on grey and harbour seals and the methods of data preparation. Thirdly, I describe the different methods to i) identify the spatial usage of both species, ii) identify the seals' foraging areas, and iii) the methods used to model the foraging habitat selection (including the description of environmental data used to represent the habitat availability).

2. CHAPTER 3: This chapter aims at identifying the most suitable approach to model the foraging habitat selection. As I aimed at studying the foraging habitat selection, I had first to identify foraging activities. For this, as seals are diving foragers, two approaches (the vertical and horizontal approaches with the "dive selection" and "Area of Restricted Search" respectively) were tested to identify their foraging areas. I first compared the results of foraging habitat selection based on input from either of these two approaches. Because most previously published studies on habitat modelling did not focus on foraging activities, I also compared these results to habitat modelling including all at-sea locations (and thus all activities).

3. CHAPTER 4: Grey seals' spatial usage and foraging habitat selection. In this chapter, I tested the influence of habitat availability (as a proxy for prey availability), and intra-specific interactions on the behavioural plasticity of grey seals in different colonies with contrasting habitats and dynamics. To answer this question, I focused on two scales: the colony scale and the individual scale. As grey seals can move over long distance, and travel between colonies, I modelled the foraging habitat selection at the individual scale to see if an individual seal had the same strategy of spatial usage and foraging habitat selection when it moved from one site to another. I first identified the foraging activities (by using the vertical approach) and then modelled the foraging habitat selection by using the foraging dives as inputs. I applied the same approach to the pooled data of tagged seals in each study site to document the grey seals' foraging habitat selection at the colony scale. Furthermore, I analysed two trip characteristics (the trip maximum extent and the trip duration) to characterize the seals' spatial usage in each colony. 4. CHAPTER 5: Harbour seals' spatial usage and foraging habitat selection. In this chapter, I studied the spatial usage and foraging habitat selection of harbour seal at the colony scale. I followed the same methodological approach as in chapter 4. However as harbour seals did not move between colonies, I only worked at the colony scale for this species.

5. CHAPTER 6: General discussion. This chapter aims at linking the results obtained in the chapter 4 and chapter 5, comparing these results obtained for both species in a context of sympatric species in the Northeast Atlantic, particularly for the two sites where they both haulout and were tagged at the same.

CHAPTER 2

Photo : Yann planque

MATERIAL AND METHODS

Species description

Grey seals

Three stocks of grey seals are usually distinguished in the Northern hemisphere: one in the Northeast Atlantic, one in the Northeast Atlantic and one in the Baltic Sea.

In Northeast Atlantic, the core grey seal population is located around Scotland where 88% of them breed [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF] mainly in the Outer Hebrides and in Orkney (Figure 2.1-1). The Sea Mammal Research Unit made a population model to estimate the number of grey seals through pups' survey, providing an estimate of 139,800 UK grey seals in 2016 [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF]. Grey seals are spread in different colonies along the European continental, Irish and British coasts (Figure 2.1-1). Telemetry and other studies have shown exchange between colonies ( McConnell et al., 1999). Grey seal numbers have increased since 1984 in the UK (Jones et al., 2015;Thomas et al., 2011).

In the North Atlantic, grey seals represent the largest phocid species and displays a sexual dimorphism. Males have a longer body length than females (210cm versus 185 cm respectively, [START_REF] Mclaren | Growth in Pinnipeds[END_REF]. Adult males and females can weight up to respectively 300 Kg and 200 Kg. Grey seals are capital breeders, which means they replenish their body reserves during summer before the winter breeding and moulting periods, when they fast (Beck et al., 2003;[START_REF] Houston | Capital or income breeding? A theoretical model of female reproductive strategies[END_REF]. During the breeding period, females and males aggregate on land in large numbers around colonies to give birth, breed, and mate respectively, decreasing considerably their at-sea activities. Females give birth to a single pup with a white coat and leave it after three weeks of maternal care (mainly suckling). There is no learning of at-sea behaviour by the pup from the mother. Outside the breeding period, seals spend the majority of their time at sea to forage. In the Northeast Atlantic, the grey seals' breeding period varies between regions following a clock-wise cline. Around the UK, females give birth from August-September in Southwest of England to November-December in Eastern England [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF].

Grey seals can alternate return trips to their haulout site in a specific area (within 100 km area where most of their foraging activities occur) but they also frequently travel over hundreds of kilometres to distinct haulout sites [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF]. The grey seal is an opportunistic and benthic feeders (Thompson et al., 1991): it is often considered as a generalist species composed with some specialized individuals. The diet varies regionally and seasonally. Grey seals feed on a wide variety of prey including mainly fish, among which sandeels, gadoids (cod, whiting, haddock, ling…), and flatfish (plaice, sole, flounder, dab), (Gosch et al., 2014). Due to the sexual dimorphism, it appears that males, females and youngs of the year adopt different foraging behaviour. Differences in seasonal patterns of diving behaviour and diet were highlighted between males and females (Beck et al., 2007(Beck et al., , 2003)). Females dived in shallower waters compared to males (presumably due to their smaller body size), but had a larger and more energetic prey composition. This can be linked to the difference of energy requirements between both sexes for the breeding and mating periods, and to the diving capacity proportional to the body mass. Trends in harbour seal numbers differ among colonies. The abundance recently and sharply declined in Orkney, Shetland and east coast of Scotland, but appears to be stable in the Western Isles [START_REF] Duck | The status of UK harbour seal populations in 2012, Species Committee on Seals (SCOS) Briefing paper[END_REF]Lonergan et al., 2011). The recent decline could be coming from direct mortality from vessel interaction, the effect of infections diseases (Hall et al., 2006;[START_REF] Harris | Evaluating the Influence of Epidemiological Parameters and Host Ecology on the Spread of Phocine Distemper Virus through Populations of Harbour Seals[END_REF], biotoxin exposure [START_REF] Hall | Evidence of domoic acid exposure in harbour seals from Scotland: A potential factor in the decline in abundance?[END_REF] or coming from interspecific competition with grey seals (Bowen et al., 2003;Svensson, 2012). This species is smaller than the grey seal, and does not display a significant sexual dimorphism. Males and females' average body lengths are 150 and 160 cm respectively [START_REF] Mclaren | Growth in Pinnipeds[END_REF]. The females give birth to a single pup with the same colour pattern as the adults (they moult their lanugo intra-utero). This allows the pups to go to the sea with their mother only a few hours after birth, learning their swimming and diving behaviour from her during the first weeks [START_REF] Renouf | Attachment between Harbour seal (Phoca vitulina) mothers and pups*[END_REF]. Generally females give birth at the end of spring (June) and adults moult every year in mid-summer to early fall, depending on sex and age, usually along sandy beaches or estuaries at low tide, rocky areas and sea-ice [START_REF] Marion | Guide des otaries, phoques et siréniens[END_REF].

In Northeast Atlantic, harbour seals are relatively sedentary, showing short distance movement from their haulout sites (10-20 Km) and long-term site fidelity [START_REF] Ries | Diving patterns of harbour seals (Phoca vitulina) in the Wadden Sea, the Netherlands and Germany, as indicated by VHF telemetry[END_REF]Tollit et al., 1998a). They usually forage within 10 Km around their haulout site (Vincent et al., 2010). They are opportunistic feeders, foraging usually in shallow bays and coastal zones. For example, in the Wadden Sea, [START_REF] Ries | Diving patterns of harbour seals (Phoca vitulina) in the Wadden Sea, the Netherlands and Germany, as indicated by VHF telemetry[END_REF] repeatedly observed harbour seals diving along the edge of the tidal channels, in water no deeper than 5 to 10m. Their diet varies geographically Europe. They forage on a wide variety of prey including sandeels, gadoids, herring, sprat, flatfish, octopus and squid (Spitz et al., 2015). Due to the absence of sexual dimorphism, there is no significant difference in foraging behaviour between males and females [START_REF] Hastings | Regional differences in diving behavior of harbor seals in the Gulf of Alaska[END_REF].

Sympatric species

In Northeast Atlantic, grey seals and harbour seals live in sympatry, as their ranges overlap on land and at sea in many areas (Figure 2.1-1), (Brown et al., 2012;McConnell et al., 1999;Sharples et al., 2012) Different haulout sites (colonies) are used by both species in the Orkney/ Shetland, Outer and Inner Hebrides (in Scotland), Southeast England, Eastern English Channel (France). Even though they have asynchronous life cycles, they perform foraging trips out at sea during most of the year. Their diets are similar, despite regional and seasonal variations (Sharples et al., 2012) both species forage on sandeel (particularly in the North Sea), gadoids, and flat fish. However, spatial niche partitioning can be expected to some extent, as for example grey seals exhibit longer trip than harbour seals.

Study areas

The objective of this PhD is to study the habitat selection of these two sympatric seal (Irish Shelf Front), where an upwelling occurs (Raine and McMahon, 1998), carrying a high production of phytoplankton, and consequently organisms of upper trophic levels. Both seal species are found in Western Ireland. One of the main colonies of grey seals on the western seaboard is located at the Blasket Islands (with 1200 individuals, Cronin et al., 2013). The Great Blasket Island offers an important sandy beach exposed at all tides for grey seals to haulout, where there can come ashore to moult in high number. The seabed around the Blasket Islands includes a dense kelp forest with undersea reefs and canyons.

South of the Blasket Islands, the Kenmare bay, hosting a population of 390 harbour seals, contains rocky intertidal sites used as haulout sites at low tide [START_REF] Kavanagh | Diet of the harbour seal (Phoca vitulina vitulina) in the west and south-west of Ireland[END_REF].

The Irish Sea is located between the eastern seaboard of Ireland and the eastern coasts of the United Kingdom. This is a shallow area (maximum depth 120m) with high tidal current. The main grey seal colonies on the southeast coast of Ireland are located at Raven Point and the Saltee Islands. Raven Point, located in the Bay of Wexford, offers an important sandbank used by grey seals for haulout at low tide. Grey seal numbers vary seasonally, ranging from 100 to 800 individuals.

South of the Irish Sea, the Bristol Channel is located on the west coast of England, between Cornwall and Wales. The Bristol Channel is surrounded by three grey seal colonies: Ramsey, Skomer, both in Wales, and Isles of Scilly further South [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF].

This area was included to study the variability of grey seals' foraging habitat selection at the individual scale.

Two others study areas are located along each seaboard of Scotland. With the Outer Hebrides, the Inner Hebrides represent an area mainly composed by fjords surrounded by irregular and deep sounds, where 4,054 grey seals pups were estimated in 2015 and 1,500 harbour seals come to haulout and forage (SCOS 2016).

On the eastern seaboard of the United Kingdom, the North Sea is located on the European Continental Shelf. Strong currents characterize this Sea, resulting from different water masses coming from the Atlantic Ocean (from the north) and the English Channel (from the south). The bathymetry is variable: the deepest point reaches 700m, while submarine sandbanks are located in shallower waters. The Doggerbank is a sandbank located at a hundred kilometres from the UK coast, in 20mshallow waters [START_REF] Kröncke | The Dogger Bank: A special ecological region in the central North Sea[END_REF]. The Doggerbank exhibits a high density of sandeels and provides seals with a good foraging spot in the North Sea (McConnell et al., 1999). A similar but smaller sandbank, the Marrbank, is located in front of the Firth of Tay, located on eastern Scotland (Jones et al., 2015). Both seal species haul out there where a large sandy haulout site emerges at low tide. This area corresponds to one of the seal management unit "East Scotland", where the total abundance of harbour seals was estimated at 284 individuals. The number of grey seals' pups in the North Sea colonies group area was estimated to 12,700 (SCOS 2016).

The Wadden Sea was also added to study the variability of grey seals' foraging habitat at the individual scale. This area is enclosed by a row of Islands, where around 5,000 grey seals come ashore to haulout [START_REF] Brasseur | SEALS IN MOTION -How movements drive population development of harbour seals and grey seals in th North Sea[END_REF].

Along the French coasts, grey and harbour seals are at the southern limit of their Northeast Atlantic range. Four study areas were defined there, one in the Iroise Sea and three along the coast of English Channel.

The Iroise Sea is located at the western point of Brittany. Two shallow areas characterise the Iroise Sea: the Molène archipelago and the Chaussée de Sein (shallower than 20m deep). Seals haul-out on the tidal rocks all the year round, whereas small islands permanently emerged are only used during the breeding and moulting seasons.

Up to 130 grey seals were counted on the haulout sites during the moult (Vincent et al., 2017). The high biodiversity is sustained by the current system, producing one of the biggest kelp forests in Europe [START_REF] Hily | Environnement naturel d'Iroise. Bilan des connaissances et intérêt patrimonial[END_REF].

The English Channel includes the last three other study areas. This is an area with shallow depths and strong tidal currents. The seals' colonies are spread along different shallow and intertidal areas, where seals can haulout on sand banks. Two are located on the Western part of the English Channel, the Baie du Mont Saint Michel and the Baie des Veys (Figure 2.2-1). The estimated numbers of harbour seals in these two colonies are 80 and 200 individuals (Vincent et al. 2017). The Eastern English Channel represents the fourth study area in France (Figure 2.2-1). In this area, both seal species forage and haulout in tidal estuaries: Baie de Somme, Baie d'Authie and Baie de Canche, or on a coastal sandbank: the Banc de Walde. The Baie de Somme constitutes the main haulout site for the largest reproductive harbour seal colony in France (i.e. almost 600 individuals were hauling out in 2015, Vincent et al. (2017)) and it was also used by an increasing number of grey seals these last years (i.e. around 100 grey seals were counted hauling out close to harbour seals, (Vincent et al., 2017).

Telemetry data

Seal handling and tagging

All tagging sites combined, one hundred and forty-nine seals were caught and tagged with Fastloc TM GPS/GSM tag, including 66 grey seals and 83 harbour seals. Grey and harbour seals were caught in the water surrounding the haulout sites with hoop nets or tangle nets, depending on the nature of haulout site (sandy or rocky haulout sites). Then, they were weighed to the nearest Kg and anaesthised intravenously using 0.8 mg/Kg of Zoletil (Vibrac, Carros, France), (Baker et al., 1990). Tags were glued to the fur of the neck using a two part quick-setting epoxy adhesive (RS components) or superglue. Grey and harbour seals were tagged under the following licences:

-Scotland: licence provided by Uk home office under the Animals (Scientific Procedure) Act 1986 and the Scottish Executive under the Conservation of Seals Act 1970.

-Ireland: licence provided by the National Parks and Wildlife Service (NPWS),

Fastloc TM GPS/GSM tag description

In this study, we used GPS/GSM tags (series 9000) developed by the Sea Mammal Research Unit (SMRU, Mcconnell et al., 2004) incorporating the Fastloc GPS system (Wildtrack Telemetry System, UK). The tag is composed of a wet/dry sensor (that allows to determine if the animal is on land or under water), a pressure sensor providing detailed information on individual dive (maximum dive depth, dive duration and shape, ten dive depth points per dive), a (water) temperature sensor, a microprocessor (allowing data treatment and storage), and a SIM card (for the GSM mobile network). Data can be stored up to six months, before the seal comes back in the range of the GSM network. Data transmission is only possible when the tag antenna is out of the water, within a GSM network coverage area (usually within 5-10 km of coastal areas). This kind of tag is particularly useful for tracking animals, like grey seal and harbour seals, hauling out frequently in coastal areas where the GSM coverage is good (Vincent et al., 2010).

GPS locations are filtered as described in (McConnell et al., 1992), in order to remove aberrant localizations (when the swimming speed becomes unrealistic, i.e. >2m/s). The accuracy of the location's estimate depends on the number of satellites spotted by the Fastloc TM system. [START_REF] Dujon | The accuracy of Fastloc-GPS locations and implications for animal tracking[END_REF] estimated that 95% of the Fastloc GPS locations obtained from 19m to 724m from their real localization. The pressure and wet/dry sensors can detect three distinct behavioural states: "hauling out", "at the surface", or "under water". These three states are temporally defined during the tracking period (with starting and ending time). A haulout event is detected when the tag is continuously dry for 10 minutes and ends when it's continuously wet for 40 seconds. In the water, the animal can be either "at the surface" or in a "dive". A diving event starts when the tag is under water deeper than 1.5m for more than 8 secs. For each dive, data recorded by the tag include starting and ending time of this event, maximum depth, (summarized by the tag every 10% of the dive duration), post dive surface duration, and the Time Allocation at Depth (TAD) index (Fedak et al., 2001). I interpolated all GPS location every 20 minutes using straight-line interpolation in order to reduce sampling bias: because GPS locations are only obtained when the tag is out of the water, the number of GPS locations obtained and transmitted from the tags depends on the movements and diving frequency of the seal. When seals are hauled out or spend most of their time at the sea surface, the acquisition of GPS locations is frequent, but when they spend most of their time diving, this frequency decreases. It is therefore necessary to take this frequency into account when modelling habitat use and habitat selection, and I used interpolated locations in order to reduce this bias.

Definition of Return-trips

The end of a haulout event and the beginning of the next one define a trip at sea. A trip during which the seal is coming back to haulout within the same hauling out area (or the same haulout) as the departure one is defined as a "return-trip" ( McConnell et al., 1999). On the opposite, when the seal moves from one haulout area to another one separated by a long distance, the trip is defined as a "travel trip", ( McConnell et al. 1999). These two definitions are independent of the distance crossed between trips, and are strictly defined by the definition of the haulout area(s) before and after the trip at sea.

In this PhD, because of our objectives that focus on "local foraging habitat selection", "return trips" were not defined from the location of one haulout, but from an aggregation of haulout sites, depending on each study area. This is especially relevant for grey seals that cross long distances, between different colonies (as an example, along the western seaboard of Ireland). Harbour seals are more coastal and few exchanges between colonies were observed (i.e., highlighting colony sedentarity), therefore for this species most if not all trips at sea were return trips.

Areas of haulout site aggregation are presented Figure 2.2-1 (i.e. "study areas"). For the Irish continental shelf, the haulout aggregation includes all haulout sites between the Blasket Islands and Belmullet. For the Irish Sea, it mainly includes haulout sites located around Raven Point and the Saltee Islands, as well as these two latter areas.

The Iroise Sea area aggregates haulout sites located in the archipelago, the Isle of Ouessant (located on the North), the Isle of Sein (located on the South) and along the coast. For the Eastern English Channel, the haulout site aggregation includes those from the Bay of Seine (for the Western part) to the limit of the French/Belgian boundary (for the Eastern part). For grey seals tagged at the Firth of Tay in the North Sea, the haulout aggregation includes the Isle of May, as it represent the main breeding area around this haulout site. For the Bristol Channel (in the Irish Sea), the haulout aggregation includes haulout sites spread along the coast of the Channel (South of Wales and North of Cornwall), and those spread along the South of Cornwall. For the Southern North Sea, the haulout aggregation includes haulout sites along the coast of Netherland (Wadden Sea).

Breeding and moulting periods

As seals are considered as capital breeders, we expect behavioural variability in both species during the breeding and moulting periods compared to the rest of their life cycle. This is particularly true for grey seals, because during the breeding period following by the reproductive period, adults spend the majority of their time hauling out, to give birth, lactate and breed, for the females, and to defend their harem for reproduction for the males. During these periods, seals are known to reduce their time for travel at sea (particularly grey seals, e.g. [START_REF] Caudron | Comparative activity budget among grey seal (Halichoerus grypus) breeding colonies -the importance of marginal populations[END_REF][START_REF] Lidgard | Diving behaviour during the breeding season in the terrestrially breeding male grey seal: implications for alternative mating tactics[END_REF] a strong reduction of their foraging activities, as well as an increased amount of time spent hauled out are observed. Indeed, their foraging behaviour is largely influenced by the need to stay close to or at haulout sites during these two periods (Boness, 1984;Thompson et al., 1994). As a consequence, this may not illustrate the seals' behaviour for the remaining period, which is mainly devoted to food supply. Thus, tracks during these periods were not included in my analyses of the seals' foraging habitat use and selection.

The software MamVisAd (developed by the Sea Mammal Research Unit, [START_REF] Fedak | MAMVIS: A Marine Mammal Behaviour Visualization System[END_REF] was used to visualize the individual diving behaviour of the seals, together with their locations and environment (bathymetry, coastline, etc.). This software was especially used to distinguish breeding activities of the seals. This can be illustrated by the example of male grey seals B35, tracked during two consecutive years, moving from the Iroise Sea to the West coasts of Ireland. Visual observation of its track highlighted a regular diving behaviour between the beginnings of tracking (i.e. June) to September; then the dives stopped between mid-November to mid-December (i.e. the seal spent the majority of its time hauling out). During this period with no dives, all locations were within a known grey seal-breeding colony. During the second year, this seal was observed in Inishshark (Ireland), defending a harem including females with their pups (O. O'Cadhla, pers. Com.). Another case of diving behaviour modification was also highlighted for the female B32. This seal tagged in the Iroise Sea stopped her dives during 3 weeks, while she was observed with her pup at the colony of Sept Iles (Figure 2.5-1). These field observations confirmed those obtained from the telemetry data and visualized with MamVisAd. Following this example, the diving behaviour of all tracked seals (for both species) was checked using this software during the reproductive period. When a modification of diving behaviour pattern was observed during this period and the seal was located in a known breeding colony, the data were excluded from the analysis of foraging behaviour. However, if there was no modification of diving behaviour, the data was kept. All the information is presented Tables 2.6-1 and 2.6-2.

Seal data used in this work

A total of 46 grey seals were selected among all sites for the analyses of habitat, transmitting a total of 225,602 GPS locations with a mean track duration of 176 days ± 66.32 days. Grey seals' characteristics and tracks (all trips and return trips) are presented Table 2.6-1, and figures 2.6-1 and 2.6-2. Fifty-six harbour seals were also selected, transmitting a total of 151,623 GPS locations. Their tracking durations were of 121 days ± 65.43 on average. Their characteristics and trips are presented Table 2.6-2 and Figure 2.6-3.

Standardisation of seal input data

The number of seals tracked from each study area ranged from 8 to 21 seals. Since the number of tagged seals differed between sites and for statistical equity, 10 seals per site were selected, when available. The selection was done in sites with more than 12 tracked seals as following:

-Individuals with a tracking period shorter than 1 month (after exclusion of data obtained during the breeding period) were excluded.

-For individuals that were tagged on two consecutive years, only one tracking data was included (i.e. for grey seals in the Iroise Sea and harbour seals in the Baie des Veys).

-Seals that spent only a short period of time in the study area were excluded (this was mainly the case for seals tagged in the Iroise Sea).

-A random selection was performed for the remaining individuals if they exceeded N=10. This was mainly the case of the Iroise Sea (21 seals tagged), the Kenmare Bay (17 seals tagged) and the Inner Hebrides (25 seals tagged). The final number of individuals and their characteristics are presented Tables 2.6-1 and 2.6-2 .

At-sea trip characteristics

To study the influence of the degree of plasticity (with intra and interspecific comparisons) and the effect of density dependence (linked to the intra and interspecific interactions) at the colony scale, I used at-sea trip characteristics: the trip duration and the maximum extent from the haulout site (both were log transformed to correct for non normal distribution). Because they are regular in time and space, we used interpolated tracks to determine these two parameters. I assumed that each trip made by an individual seal was independent from the others. First, the Shapiro and Bartlett tests (from Stat R package with the function shapiro.test and Bartlett.test) were used to test respectively the normality and the homoscedasticity. If the normality and homoscedasticity were validated, an ANOVA was used to compare inter and intra sites plasticity, otherwise, a Kruskal Wallis test was used (from "Stat" R package with kruskal.test and aov functions, R version 3.0.3). If the intra and/or inter-site variability were validated, a post hoc test was carried out for pairwise comparisons: Tuckey HSD test (tuckey HSD function in Stat R package) in the case of ANOVA and dunn test (dunn.test function in the "dunn.test", R package) for Kruskal Wallis test.

Assessment of the seals' foraging locations

Modelling the seals' foraging habitat selection first requires the identification of their foraging areas. Different methods have been developed, especially from telemetry studies (Bailleul et al., 2008;Pinaud and Weimerskirch, 2007). In this study, I tested two different approaches: one based on the horizontal movements and the second one based on the vertical movements (the choice of these two methods is explained in Planque et al. (in prep, Annexe II).

Analysis of horizontal movements: the First Passage Time

The First Passage Time (FPT) analysis can be used in order to detect changes in the animal's general movement pattern along its trip (Fauchald and Tveraa, 2003). These changes are illustrated in Figure 2.9-1, representing an individual trip in a heterogeneous habitat. Along its trip, the animal can adopt two behaviours:

-The "travelling behaviour" is represented by a strait and horizontal movement where the animal moves at a constant and rapid speed.

-The second one occurs when the animal enters a patch of potentially high prey density, and then starts to move "into circles". This is called an "Area of Restricted Search" (ARS,) (Kareiva and Odell, 1987). The seal is then reducing its speed and increasing the sinuosity of its trip, in order to focus on the patch of preys.

Details on the method's principles and on its application on the dataset are presented in Annexe II ( Planque et al. in prep).

Analysis of vertical movements: assessments of foraging dives

The second methodology, using the vertical approach, is based on the analyses of the dive shape. As we aimed at selecting the foraging dives, we used a method linked to the "Optimal Foraging Theory" (Stephens and Krebs, 1986), taking into account that grey and harbour seals are benthic feeders (Thompson et al., 1991). We applied two diving criteria indicating the seals' benthic foraging behaviour: the dive shape (based on Time Allocation at Depth, Fedak et al., 2001) and vertical descend speed (Vincent et al., 2016). As for the horizontal approach, the details and application of this method on the dataset are presented in Annexe II ( Planque et al. in prep).

However, these criteria were assessed individually because of possible individual dive strategies linked with physiological or behavioural characteristics (Austin et al., 2006;Beck et al., 2003).

Area of Restricted Search

Patch of preys « Travelling behaviour »

Habitat selection

Use availability design

Foraging locations obtained from the analysis of the dives and/or the detection of ARS were used in order to model the seals' foraging habitat selection. Habitat selection with telemetry data can be modelled following three designs: random, case-control and use-availability design (Johnson et al., 2006;Keating and Cherry, 2004). However, the two first designs are uncommon in wildlife studies while the use-availability is perhaps the most common sampling design in the habitat selection design. As telemetry studies only give true presences (i.e. tracking data) and not true absences, we used the useavailability design. The use-availability design requires a random sample of true presences and a second random sample drawn from the habitat, but without knowing whether points in the second sample contain presence or absence (representing the availability of the habitat). Habitat selection analyses investigating both continuous and categorical variables typically infer selection by comparing measured environmental characteristics of used points, represented by telemetry locations, to the characteristics of random points that represent available habitats (Aarts et al., 2008;Johnson et al., 2006;Lele and Keim, 2006). This represents the use-availability design (Keating and Cherry, 2004).

Foraging data obtained from either the horizontal or vertical approach were used separately to model the foraging habitat selection in the study areas. However, most published studies on marine predators' habitat selection conducted from telemetry tracks used all at-sea points, including all at-sea activities (i.e. resting, travelling, foraging, Aarts et al., 2008;Huon et al., 2015;Wakefield et al., 2011). Thus, I also modelled the habitat selection from the interpolated data (i.e. all at-sea locations), in order to compare the results to those obtained from the two methods of the selection of foraging points to see if there is difference in the model outputs among the three model inputs. These three analytical methods (i.e. horizontal approach, vertical approach and all locations) were used as "use" points, representing the true presences. The random points were created locally within each study area using the package sp with the R version (R version 3.0.3). These random points correspond to the habitat availability.

Typically, the number of random points varies between 1 and 5 times the number of telemetry locations [START_REF] Pearce | Modelling distribution and abundance with presence-only data[END_REF]Wakefield et al., 2011). In this study, we chose the number of random points to be twice the number of telemetry locations, in order to balance the restricted size of the study area with an appropriate density of points. Random points were created within buffers for each study area. Each buffer was created as following:

-The size of each buffer had to be around three times the size of the Minimum Convex Polygon (MCP, [START_REF] Burgman | Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning[END_REF] of the seals' return trip locations.

-

The buffer was limited by the continental shelf, as seals do not travel beyond this area (e.g. for the western seaboard of Ireland),

For study area where both species are present, such as Saint Andrews and the Eastern English Channel, one buffer was created for grey seals and another one for harbour seals, as they use offshore and inshore areas, or only inshore areas, respectively. The buffers are presented figure 2.2-1.

Explanatory variables

Different environmental variables were used in order to characterise the foraging habitat selection by grey and harbour seals. These variables were chosen depending on the role that they seem to have in seal foraging ecology (Aarts et al., 2008;Bailey et al., 2014;Huon et al., 2015). As no data on prey availability was available for most study areas, I used these environmental data as a proxy of prey availability: the bathymetry, the tidal current and sediments.

The bathymetry was selected since seals are known to be benthic feeders. The (Folk, 1954) Since grey and harbour seals can use tidal current to move and to forage, this parameter was also used as an explanatory variable (Zamon, 2003(Zamon, , 2001)). However, unlike bathymetry and sediment datasets, a general tidal current dataset with fine resolution was not available. Different datasets were used for each study area, scaled to similar resolutions for inter-site comparisons.

-For the French study areas, data set from the MARS2D model with a resolution of 250m was used, covering the four different study areas in the Iroise Sea and the English Channel. Tidal current data was available hourly from the Previmer database (Lecornu and De Roeck, 2009) ‡ . For each study area, the hourly dataset ranging from the beginning to the end of the tracking period were averaged in order to create a six months tidal current average. This corresponds to the seasonal tracking duration of each study area, allowing the quantification of the tidal strength in space irrespective of instant tidal phases (ebb, slack or rising tide), (Figure 2-11-3 A). ‡ https://marc.ifremer.fr/resultats/courants/ -For Irish study areas, the tidal current data was produced by the Irish Marine Institute § . This data corresponds to the outputs of numerical model providing surface tidal current at 3 hours intervals with a spatial resolution varying between 1.2Km and 1.5Km. A 6 months tidal current average also corresponding to the seasonal tracking duration in Ireland was calculated, following the same process as for the French study sites. This dataset covers the Irish Continental shelf and the Irish Sea. Nevertheless, tidal current dataset at fine scale was not available for the Kenmare area, and thus this variable was missing in analyses of this area (Figure 2.11-3-B).

-For the Scottish study areas, the tidal current data was provided by the WEBvision- Additionally to these three environmental variables, two other explanatory variables were also added. As seals are central place foragers, the distance from last haulout was used to describe the environmental accessibility (Aarts et al., 2008). We calculated the geodesic distance from each location (seal location or random point) to

C B

the last haulout visited. This distance was calculated using the LC.dist function in the marmap package R, (Pante and Simon-Bouhet, 2013). The seals may use coastal features such as landmarks, so that the distance from the shore was also calculated and used in the models. It was calculated as the straight-line distance to the closest point along the coast using ArcGIS with the "Nearest" function.

Habitat selection modelling

Generally, the selection of the abiotic and biotic features of the habitat by an animal is not linear. Furthermore, telemetry data are temporally and spatially correlated at the individual scale (Aarts et al., 2008), but it not the case of the availability, as point were created randomly. To take into account these two principles, Generalized Additive Mixed Models (GAMM) were used. These models were already used in different marine predators' habitat selection studies based on telemetry surveys [START_REF] Photopoulou | Spatial variation in maximum dive depth in gray seals in relation to foraging[END_REF]. However, other studies (i.e. Bailey et al., 2014) used Generalized Estimating Equations models (GEES). GEEs do not seem to be appropriate to use-availability design (as the correlation structure of the combinations of used and available points do no follow parametric forms.

Simply said, the GAMM uses a combination of smoothing curves that allows representing a non-linear relation between the explained variable and the explanatory variables (representing the Generalized Additive Models, GAM). Then, a random effect is added to this model in order to free the model of violation of the data independency hypothesis (Aarts et al., 2008). Thus the equation of GAMM can be represented as following: 2016), by using the option "bs=re" to take into account of the random effect. Different functions can be applied to perform GAMM. However, most of them have a problem of convergence when the dataset is too large (Wakefield et al., 2011). The use of the gam function avoided this problem of convergence due the size of datasets.

Yij = α + s1 (x1 i ) + s2 (x2 i ) + … + sk (xk i ) + μ j +
A binomial family argument was used with a logit link function to estimate the parameters of inverse-logit selection based on seal foraging locations and random points, for all study areas (Johnson et al., 2006). Locations and random points were the response variable, taking respectively the value 1 and 0. Explanatory variables were included as fixed effects. However, depending on the colony or individual scale, I used respectively individual and trip number as random effects. The trip number was used as a random effect for the individual effect, because I considered that trips of the same individual seals were independent, but tracking points within a trip were dependent and thus correlated. The bathymetry, tidal current, distance from shore and distance from the last haulout were included in the model as continuous variables, while sediment types were included as categorical variable. When one sediment type was over-represented, the model was forced to take into account this sediment type as a reference (otherwise all sediment types were treated by alphabetical order). The multi-colinearity between covariates was assessed using the Variance Inflection Factor (VIF and Pearson Coefficient. Different models were carried out for each study area at the colony scale but also at the individual scale. For each study area, the best model was selected using the AIC (Akaike, 1973). Furthermore, I calculated the importance of each covariate with the predictive function of the "GAMM", providing an index of the relative importance of each covariate in the chosen model. The maps of habitat selection predicted by the models were created with ArcGIS for all study areas.

Remark: The comparison between the horizontal approach, the vertical approach and all locations was applied for the Iroise Sea study Area. Thus, the approach that suited the best the forging habitat selection was then chosen and applied to the other study areas. I choose the Iroise Sea as "experimental site" to test this comparison as I obtained good results for preliminary habitat modelling (Huon et al. 2015, Annexe I).

SELECTION OF INPUT DATA FOR THE MODELLING OF SEALS' FORAGING HABITAT SELECTION

Most habitat modelling approaches using telemetry data include all locations from the studied animals in order to model their selected habitat (Aarts et al., 2008;Huon et al., 2015;Wakefield et al., 2011). In order to focus on the foraging habitat of the seals, I chose to only include foraging locations as input data, rather than all locations.

Different methods are used to identify the foraging activities of marine top predators from telemetry data (Bailleul et al., 2008;Pinaud and Weimerskirch, 2007). Planque et al. (in prep, Annexe II) applied and compared two methodologies, mentioned here as the vertical approach and the horizontal approach, on grey and harbour seals tracked in Eastern English Channel. In this chapter, I first summarized the results obtained for this study area, and then applied these two approaches for the identification of seals' foraging locations on data obtained in the Iroise Sea. I then compared the results of habitat modelling while including i) all seal locations at sea, ii) only foraging locations as assessed from the diving data (vertical approach), and iii) only foraging locations in Areas of Restricted Search (ARS) as assessed from the First Passage Time analysis (horizontal approach). This comparison was conducted on data obtained on grey seals in the Iroise Sea. Results discussed in this chapter only relate to the choice of methodology for the habitat selection modelling, while results on grey seals' foraging strategies will be discussed in Chapter 4.

Comparison of two approaches for the identification of foraging areas, and habitat use

Foraging activities identification in the Eastern English Channel

The identification of foraging areas with horizontal and vertical approaches was studied on harbour and grey seals in the Eastern English Channel. Planque et al. (in prep, ANNEXE II) performed horizontal and vertical approaches while considering inter-individual foraging strategies, indeed in the horizontal dimension they performed FPT from a very small scale (i.e. minimum scale of ARS assessed was 0.5 km of radius)

as they tried to also capture searching behaviour of seals which foraged at a small scale (i.e. especially for harbour seals); and in the vertical dimension they decided to choose individual dive criteria in order to take into account inter-individual variability of foraging strategies.

First, the foraging areas identified by both the horizontal and vertical approaches were compared at the colony scale, by visual observation of the resulting foraging location densities (grid cells' densities and 75% Kernel densities). Secondly, the Index of Differences in Spatial Pattern (IDSP, [START_REF] Lee | Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data[END_REF], Cronin et al. 2012) was used to quantify the possible overlaps of foraging areas identified by each approach. This index can range from 0 (i.e. corresponding to a complete overlap) to 1 (i.e. corresponding to no overlap at all). Our results showed that the same main foraging areas were highlighted by both methods for grey seals, with an IDSP value of 0.35, indicating a strong overlap between the two spatial distributions. For harbour seals, a strong overlap was spotted with the visual observation with the respective grids. However, one area highlighted by the vertical approach was not identified by the horizontal approach. The IDSP value was higher than the one for grey seal (0.49). We also compared the amount of time spent foraging by the seals according to both methods, and found results: 33.8% and 26.5% of tracking time spent foraging by grey seals, from the horizontal versus the vertical approach respectively, and 26.9% and 21.5% for the harbour seals, from the horizontal versus the vertical approach respectively. These are average figures for all seals tracked in this colony, but inter-individual differences were shown. The three approaches highlighted the same two main areas of habitat use and foraging areas (cf. 75% Kernel areas, Figure 3.1-2). These "hotpots" are located between the Mainland and the Molène archipelago and in front of the Abers. Furthermore, a smaller area was also identified by the three methods on the Southwest of Ouessant. However, other small areas were highlighted by some approaches but not all, such as the one located around Ouessant (identified from all locations or only foraging dive) or another one around Sein (identified from all locations or only ARS locations). for each approach, two models were created, one including all the covariates and excluding only the bathymetry; and the other one including all the covariates but excluding only the distance from shore. In each case, I selected the optimal model with the lowest AIC value (Table 3.2-1), excluding the distance from the shore. Thus, all selected models included the distance from the last haulout, the bathymetry, the sediment types and the tidal current. The explained deviances by the models were similar, representing 79.3%, 80.6% and 78.3% for all locations, ARS locations and foraging dive locations, respectively. The parts of each variable in the deviance explained were similar for the habitat selection model and the foraging habitat selection model using the vertical approach. The distance from the last haulout accounted for most of the model's explained deviance (81% and 76% respectively), while the bathymetry, tidal current and sediment types accounted for 11% and 15%, 3% and 4% and 1% and 2% respectively. The foraging habitat selection model using the horizontal approach differed as the distance from the last haulout accounted for 97% of the explained deviance (Tables 3.2-2,3.2-3,3.2-4; Figure 3.2-1). The distance from the last haulout had a negative influence on habitat selection, i.e.

grey seals selected habitat close to their haulout. The bathymetry had a negative influence: habitat selection decreased when depth increased up to 50m. The tidal current had a weak influence. In both cases, grey seals selected mixed sediments, avoiding coarse sediments, rocky, muddy, and sandy areas. The model outputs for the foraging habitat selection with the horizontal approach showed slight difference compared to the two others cases. The principal difference was noticed from the C distance from the last haulout. In that case, the distance from the last haulout had a weak influence up to 50 Km, and then the selection decreased sharply. Furthermore, grey seals selected mixed sediments (as for all locations and foraging dive locations)

I

C B

Grey seal mainly selected their habitat and foraging habitat around the Molène archipelago. However, areas of predicted foraging habitat selection were more strongly highlighted than from the habitat selection. These areas were located around the isle of Ouessant, Crozon, Abers area and isle of Sein.

Discussion

As the habitat selection is mainly driven by the need to forage, I focused this PhD on the foraging habitat selection. The foraging locations can be identified from two approaches: one horizontal and the other one vertical. The First Passage Time aiming to identify Area of Restricted Search (ARS) represented the horizontal approach; and the vertical approach consisted of criteria developed to identify foraging dives (linked to the dive shape and the descent speed). First, I aim at comparing the influence of these two approaches in the identification of the foraging habitat selection. I also modelled the habitat selection from all locations and compared the results with those obtained for the foraging habitat selection, to compare both approaches.

Foraging habitat selection: horizontal versus vertical approaches

The same main foraging areas were highlighted from the vertical and horizontal approaches (i.e. Molène archipelago -Mainland, Goulet, and Abers area, Figure 3.1-1).

However, secondary foraging areas differed between both methods. Even though, the deviance explained by both the horizontal and vertical approaches were similar, the variables' influence and their part in the explained deviances were slightly different.

The distance from the last haulout had a strong influence in the horizontal approach, starting after 50Km. Furthermore, grey seals selected rocky and mixed sediments. On the opposite, in the vertical approach, the influence of the distance from the last haulout was weaker and only mixed sediments were preferred.

To understand and compare the results (i.e. model outputs) of the foraging habitat selection by both approaches, first, I have to understand the model inputs. Planque et al. (in prep) first used the horizontal and vertical approaches on harbour and grey seals tracked in Eastern English Channel. Results from both methods were consistent, highlighting the same main foraging areas for the two species (i.e. IDSP value were low, indicating a strong overlap of foraging areas identified by both methods). However, the vertical approach highlighted the same foraging areas as well as more widespread less densely used foraging areas. This difference can be explained by the characteristics of both methods. First, the use of both approaches induced differences at the scale of behaviour studied, indeed the horizontal approach was applied at the trip scale (several kilometres and hour or days) while the vertical approach was applied at the dive scale (several meters and minutes) inducing a difference in behavioural scale.

Indeed, the horizontal approach can detect the movement changing at scale of kilometres along trip of several kilometres (corresponding to a temporal scales of several hours or days), illustrating the scale of environment. While, vertical approach can catch the behaviour at diving scale (corresponding to the temporal scale of minutes), may represent the scale of prey. Bailleul et al., (2008), admitted that the incorporation of vertical dimension in foraging analyses can provide a better estimate of the scale of foraging behaviour for diving predators as seals.

We have to keep in mind that the horizontal approach can only provide the general pattern of a searching behaviour at a trip scale. This study in the Eastern English Channel underlined the importance to assess the ARS at the shortest possible scale in order to take into account the searching behaviour of the seals close to the haulout sites. This was particularly true for harbour seals that remained very coastal in this area, a behaviour that could be documented at a fine scale thanks to the accuracy of the Fastloc GPS system. Furthermore, this approach is determinist by identifying only two states: one with a high speed and a directed horizontal movement (i.e. low residence time, interpreted as travelling between prey patches), and the other one with a low speed and sinuous horizontal movements (i.e. high residence time, interpreted as foraging in prey patches), (Kareiva and Odell, 1987). Nevertheless, ARS does not always match with foraging behaviour but can also match with resting behaviour (Owen- Smith et al., 2010;Pinaud and Weimerskirch, 2007;Sommerfeld et al., 2013).

Grey seals and harbour seals can rest at the surface or on the bottom. In the last case, they decrease their speed, increasing the residency time, and then this behaviour can be interpreted as an ARS (Thompson et al., 1991). This happens particularly in areas close to their haulout sites. In addition, Areas of Restricted Search are mainly identified at the maximum trip extent (Thums et al., 2011).

It was previously shown that the incorporation of the vertical dimension in data analyses can provide a better estimate of the scale of foraging behaviour of diving predators such as seals (Bailleul et al., 2008;Bestley et al., 2015). As seals can forage opportunistically during their linear movements (Thompson et al., 1991;Vincent et al., 2016;Weimerskirch et al., 2007), studying their vertical movements allow to also detect foraging dives which could occur during transit phases between the main foraging areas. Indeed, the vertical approach developed by Planque et al. (in prep) permitted to consider these possible opportunistic foraging events, even during trips parts, which could be associated with travelling by horizontal approach.

In the Eastern English Channel, the vertical and horizontal approaches identified both the same main foraging areas, with some differences linked to the approaches' respective biases.

The results found in the Iroise Sea were consistent with those in the Eastern English Channel. A strong overlap was also highlighted with the two main foraging areas identified with horizontal and vertical approaches. However, different secondary foraging areas were identified. These differences were due to the biases/advantages of the horizontal and vertical approaches. The vertical approach identified a small foraging area on the "Chaussée de Sein" bedrock. These foraging dives were probably identified by the ability of the vertical approach to identify the foraging activity in the linear movement of the seal. One secondary foraging area was identified around the Isle of Ouessant. Some seals were observed sleeping in underwater caves (Vincent pers. Com.). This questions the ability of the horizontal approach to distinguish foraging activities versus resting activities in "Areas of restricted Search". However, some ARS were identified at fine scale, allowing me to take into account the probable coast foraging behaviour at fine scale (i.e. those performed around haulout sites).

By using these inputs, those differences observed in the identification of foraging areas logically induce differences in the habitat modelling outputs. The foraging areas identified by the horizontal approach were more concentrated around haulout sites.

This was highlighted by the influence of the distance from the last haulout in the model outputs, which is dominating in that case in comparison to the models tested from foraging dives. The application of the vertical approach (i.e. foraging dives) permitted to avoid the overestimation of usage around the haulout sites, detected with the ARS.

This explained the fact that grey seals selected rocky sediments with the ARS locations and not with the foraging dives locations (as in the Iroise Sea the main haulout sites are situated along the bedrocks in the Molène archipelago). However, as the part of the sediment types in the deviance explained is low in both cases (<5%), the consequences regarding to this variable have to be interpreted carefully.

Furthermore the foraging habitat selection prediction maps pointed out the limit of ARS in identification of foraging areas at the trip maximum extent. One aim of this chapter was to compare the usage of the ARS locations and the foraging dives locations in foraging habitat selection. The results highlighted some differences between both approaches. The results obtained with the ARS locations have shown an overestimation of habitat selection around the haulout sites. This was perceived with the part of the distance from the last haulout in the explained deviance (96%), graphically and with the map of the prediction of the foraging habitat selection. In comparison to the model including inputs obtained with the horizontal approach, the influence of distance from the last haulout was lower. Thus, I can interpret the high influence of the distance from the last haulout by an over-estimation of activities located around the haulout by the horizontal approach, as some ARS might correspond to resting activities. Consequently, this can underestimate areas of foraging habitat selection.

This was supported with the results obtained for the model using the foraging dives locations, as I assumed that the resting locations were discarded. Thus, for the Iroise, the vertical approach seems to be more suitable for the model of foraging habitat selection than the horizontal approach.

Foraging habitat selection versus habitat selection

By using all at-sea locations, the behavioural state of the seal (i.e. travelling, foraging and resting) is not taken into account and the "general habitat selection" is modelled irrespective of the functionality of this habitat for the animal. Overall, the same main areas were highlighted from modelling the general habitat use or the foraging habitat use. However, the areas highlighted by the habitat use can be perceived as intermediate areas between the foraging areas spotted by both horizontal and vertical approaches, as the secondary foraging areas revealed by with the ARS locations and foraging dives locations are all shown on habitat use. This intermediate "state" is also found in the model outputs. The explained deviance by the model using all locations was similar to the two other approaches. However, the influences of variables (% in the explained deviance and graphics) were more similar to the ones obtained with the vertical approach than the ones of the horizontal approach. In addition, contrary to the foraging methods, the hotspots highlighted by the grid of habitat use and model predictions were slightly different. The high probability of selection primarily occurred around the bedrock of Molène. This was in accordance with the results of Huon et al., (2015); where grey seals spent 43% of their time on these bedrocks. In this area, grey seals foraging really close to their haulout sites and rest in the kelp forest surrounding them. Thus the predictions of habitat selection around these haulout sites were overestimated This intermediate "state" by using all at-sea locations compared to the foraging methods appeared consistent. All at-sea locations combined the overestimation of foraging activities around haulout sites with the characteristics of foraging dives identified "en route". I could expect a widespread area of habitat selection compare to those foraging habitat selections, as I didn't focused only on foraging but on whole activity at sea. However, the high density of points around the bedrock (corresponding to foraging, travelling and resting activities) had an influence.

Vertical approach selection

In the Iroise Sea, grey seals mainly adopted coastal behaviour compare to other colonies ( McConnell et al. 1999), due to the configuration and the habitat availability in the Iroise Sea. Overall, the results obtained by the 3 approaches were similar.

The Iroise Sea was chosen as "test site" as the first results of habitat modelling at the beginning of the PhD were relevant (Huon et al. 2015). However, it should be interesting to test these methods in another study area were seals had travelled further offshore, as the Irish Continental shelf for example. The comparison of the three methods highlighted the fact that the inputs of the model can have an impact on the model outputs, particularly with the limit and advantage of the horizontal and vertical approaches. Considering the advantages of vertical approach the overestimation of selection around haulout sites due to the resting activities not took into account by the horizontal approach, and the contrasted habitat of each study area, I choose to use the vertical approach to identify the foraging habitat selection in the next analyses (next chapters).

GREY SEAL FORAGING HABITAT SELECTION

This chapter presents the analyses of spatial usage and foraging habitat selection of grey seals in a number of sites around the British Isles. First, I analysed how grey seals used their habitat for foraging and how this selection varies from one region to another by comparing grey seals' strategies between distinct colonies. I also compared individual strategies of foraging habitat selection when the same seal moved from one region to another. The results on grey seal foraging habitat selection in the Iroise Sea are not presented in this chapter, as there were presented in the Chapter 3, but they are discussed in the last part of this chapter.

Intra and inter-site spatial usage variability

The intra and inter-site spatial usage variability was studied using two metrics: the trip duration and the trip maximum extent. Statistical tests were performed to identify the possible variability of these two metrics within sites and between sites. Only trips longer than 3h were used (trips shorter than 3h were considered to be linked to the haulout activities). In each case, we applied the Kruskal Wallis test because the normality and homoscedasticity were not respected. As there were some long trip outliers from a number of individuals in different sites, we compared the median values of trip metrics between study areas.

Trip duration

I found that grey seals' trip durations were significantly different between sites (p<0.05,

Trip maximum extent

Trip maximum extent was also significantly different between areas (p< 0.001,

Foraging habitat selection

Individual variability in foraging habitat selection according to local habitat

Four seals (two tagged in the Iroise Sea and two in the Eastern English Channel) travelled from their tagging sites to other colonies with contrasting habitat and prey availability. They performed enough return-trips from these colonies to study their foraging habitat selection in these different sites. Twenty eight percent of their dives were identified as foraging dives (i.e. representing 46 312 foraging dives on a total of 164 314).

• B31

The seal B31 travelled between the Iroise Sea and the Irish Continental Shelf, and performed return-trips from these two areas. Its foraging habitat selection was modelled in both regions and model outputs were compared in order to document the individual variability in foraging strategies depending on available habitat. While modelling B31's habitat selection in the Iroise Sea, colinearity was detected between the distance from shore and the bathymetry, and between distance from shore and the distance from last haulout (Pearson coefficient >0.75). Thus, two models were set: one including distance from the last haulout and bathymetry (excluding the distance from shore); and another one including distance from shore and excluding the two other covariates. The optimal model for foraging habitat selection of B31 in the

Iroise Sea was the one with the lowest AIC, including distance from haulout and bathymetry (Table 4.2-1). The selected model included the distance from the last haulout, the bathymetry, sediment types, and tidal current as explanatory variables.

Due to the heterogeneous distribution of sediment types, rocky and coarse sediments were pooled together; this was also the case for sand and mud. The deviance explained by this model was 62.6%. The distance from the last haulout and the bathymetry explained most of the deviance of the model (45.4% and 48.3% respectively), while the tidal current and sediment types explained less than 10% together (Figure 4.2-2).

Distance from the last haulout had a negative influence on foraging habitat selection:

B31 selected foraging habitat close to its haulout sites. The depth from 50m to 100m had a positive influence on B31 foraging habitat selection. The optimum tidal current speed selected was 0.7m/s. This grey seal selected rocky and coarse sediments, avoiding mud, sand and mixed sediments. While modelling B37's habitat selection in Cornwall, colinearity was detected between the distance from shore and the bathymetry (Pearson coefficient>0.75). To deal with this colinearity, two models were created: one including distance from shore and the other one including the bathymetry. The optimal model, explaining 70.8% of the deviance, included the distance from shore, the distance from the last haulout, sediment types and tidal current (Table 4.2-6). As in the Iroise Sea, the distance from the last haulout site explained most of the deviance (84%, Figure 4.2-6). The distance from the last haulout had no influence on foraging habitat selection for the first 120Km, but then negatively influenced this habitat selection. B37 selected foraging habitat close to shore (within 60 km). Tidal current had a negative influence on its foraging habitat selection. No colinearity between all covariates was detected in the Eastern English Channel during the foraging habitat selection explanatory analyses. Thus, the selected model included: the distance from the last haulout, the distance from shore, the bathymetry,

A B

the sediment types, and the tidal current. The deviance explained by the model was 37.7%. Each covariate took part in the model's explained deviance as following: 70%

for the distance from the last haulout, 20.3% for the distance from shore, 10.3% for tidal current, . The bathymetry and sediment types accounted for less than 1%.

In the English Channel, G04 selected foraging habitat close to its haulout sites and close to the shore. The bathymetry had a weak influence on foraging habitat selection up to 40m, and then a negative influence. The foraging habitat selection increased with an increasing speed of tidal current. G04 selected foraging habitat associated to rocky and sandy sediment types (Figure 4.2-8, Table 4.2-7). In the Wadden Sea, I did not detect colinearity between covariates, allowing the optimal model to include all the covariates. As in the English Channel, this model included the distance from the last haulout, the distance from shore, the bathymetry, sediment types and the tidal current. The deviance explained by the model was 40%.

The distance from the last haulout was the main variable explaining the deviance (96%). The distance from shore, the bathymetry, the tidal current and sediment types together explained the other 4%. In the Wadden Sea, G04 selected habitat close to its haulout sites and to the shore (Figure 4.2-9, Table 4.2-8).

In this area, the foraging habitat selected by G04 was located on coarse sediments, and avoided sandy areas. Regarding the foraging habitat selection modelling for this seal in the Eastern English Channel, no colinearity was detected between covariates, allowing the inclusion of all of them in the model. However, because of the heterogeneous distribution of the different sediment types, the coarse and mixed sediments were under-represented compared to the other levels, and were pooled together. The optimal model included the distance from the last haulout, the distance from shore, the bathymetry, the sediment types and the tidal current. The deviance explained by the model was 44.3%. The distance from the last haulout and from the shore accounted for most of the deviance explained by the model (61.6 and 20.0% respectively), while the bathymetry, the sediment types and tidal current accounted for less than 20% together . The distance from the last haulout positively influenced habitat selection from 0 to 40 Km and then negatively influenced it. Foraging habitat selection decreased rapidly in water shallower than 17m and then increased to reach a maximum at around 35m. The habitat selection slowly decreased from 0 to 20 Km from shore and then decreased more rapidly. In this area, G11 selected areas of tidal currents at around 0.7m/s. This seal avoided coarse and mixed sediments. In the North Sea, colinearity was detected between distance from shore and distance from the last haulout, as well as distance from shore and tidal current. Thus, two models were performed: one including all the covariates and excluding the distance from shore; and the other one including the distance from shore but excluding the distance from the last haulout and the tidal current. However, contrary to the other cases, here we selected the model with the highest AIC (Table 4.2-10), including the distance from the last haulout, the bathymetry, the sediment types and the tidal current. This model was selected because it appeared more biologically relevant compared to the second one due to the site configuration. The deviance explained by the selected model was 18.7%, and was mainly explained by the bathymetry and the tidal current (58.3% and 25.8% respectively). The other covariates: the distance from the last haulout and the sediment types explained less than 21% of the deviance

B

(Figure 4.2-12; Table 4.2-11).

Comparing global grey seal foraging habitat selection between colonies

While the previous part aimed at studying the variability in individual seals'

foraging habitat selection when these seals were moving from one area to another, this part aims at comparing the global foraging habitat selection, by all tracked individuals in each colony, from one study area to another.

• Iroise Sea

Results of grey seals' foraging habitat selection in the Iroise Sea are presented in previous chapter.

• Eastern English Channel

A total of 61,093 foraging dives were selected, among all individuals in the Eastern English Channel. In the Eastern English Channel, three main areas with a high foraging effort were highlighted: one in the middle of the English Channel, one along the coast and another one in the strait of Dover (Figure 4.2-13). While modelling the habitat selection of grey seals in the Eastern English Channel, colinearity was detected between the bathymetry and the distance from shore, as well as between the bathymetry and the tidal current (Pearson coefficient>0.75). To deal with this colinearity, two models were created: one including all the covariates excepting the bathymetry, and the other one excluding the distance from shore and tidal current. In this area, the optimal model explained 30.9% of the deviance and included the distance from shore, the tidal current, the distance from the last haulout and the sediment types (Table 4.2-12). The distance from the last haulout and the sediment types accounted for most of the explained deviance (56 and 24% respectively), while the distance from shore and the tidal current accounted for 20% . The foraging habitat selection decreased rapidly with the distance from the last haulout up to 100 Km, then increased at distances increasing from 100 to 200 Km. The distance from shore had a negative influence on foraging habitat selection, from 0 to 20 Km, but a positive one above 20 Km. The selection decreased rapidly with tidal current speed up to 0.6 m/s, then increased slowly between 0.6 and 1.0 m/s. Grey seals selected muddy sediments for their foraging habitat, avoiding coarse and mixed sediments, rocky and sandy areas. The data exploration for the foraging habitat selection modelling did not detect colinearity among covariates; so all variables were included in the model. However, as the sand was the predominant sediment type compared to the other types, the model was forced to take into account this type as a reference one. The optimal model for habitat selection of grey seals tagged at the Great Blasket Island explained 66.6% of the deviance. The distance from the last haulout and the bathymetry accounted for most of the explained deviance in the model (58 and 25% respectively), while the distance from shore, the tidal current and the sediment types accounted for less than 20%

Discussion

The objective of this work was to study the influence of behavioural plasticity on spatial usage and foraging habitat selection of grey seals depending of the habitat and prey availability. The prey availability for the seals is under pressure of intra and/or inter-specific competition leading to density dependence. In the Northeast Atlantic, grey seals' population is spread into different colonies, with different local abundances, dynamics, habitats and prey availability.

Grey seals are known to be central place forager and benthic feeders ( McConnell et al., 1999;Thompson et al., 1991). They feed on a variety of species (sandeels, gadoids) and are often referred as a generalist species but composed by some specialised individuals (Gosch et al., 2014). Their diet varies regionally (Hammond et al., 1994;Prime and Hammond, 1990; Ridoux et al., 2007;Thompson et al., 1996). They have a high mobility as they can travel several hundreds of kilometres. I focused on 5 study areas in Northeast Atlantic, with contrasted dynamic and habitat availability, allowing me to identify the possible effect of behavioural plasticity on spatial usage and foraging habitat selection. However as data on prey availability was not available, I used a combination of different environmental variables as proxies aiming to reflect the prey's habitat, and consequently their distribution.

The explained deviance by the models on foraging habitat selection were 78.3%, 30.9%, 66.6%, 66.6% and 51.8% for the Iroise Sea, Eastern English Channel, the Irish continental Shelf, the Irish Sea and the Firth of Tay respectively. As the deviance explained by the model of the Iroise Sea was relatively high, I assumed I caught the greatest part of the variability explaining the foraging habitat selection of grey seal in this area, while in the Eastern English Channel the explained deviance was relatively low. This implies that other drivers than the distance from the last haulout, the sediment types, the distance from shore and the tidal current explained the foraging habitat selection in the Eastern English Channel.

The two main factors explaining the most important part of the model deviance (more than 80% in most of the case) was the distance from the last haulout and the bathymetry. For all study areas, grey seals selected foraging habitat close to their haulout sites, in shallow waters. These results can be explained by the need for the seals to go back to land for rest, moult, socialising etc. In Northeast Atlantic, the bathymetry on the continental shelf decreases gradually from the coasts to offshore areas and as grey seals select foraging habitat close to their haulout sites, they are consequently in shallow waters. This negative influence of the distance to the haulout sites was also shown on grey seals in the Farne Islands (Aarts et al., 2008), where their habitat selection decreased after a few tens of kilometres from the haulout sites. Sjöberg and Ball (2000) showed that grey seals spent most of their time within the vicinity of their haulout site with a preference for short distances from haulout (10-15 Km).

In Northeast Atlantic, the core grey seal population occurs in UK waters with an estimated 139,800 individuals (SCOS, 2016). The analyses of inter-sites variability of spatial usage showed that grey seals tagged in the Firth of Tay had the highest trip maximum extent and the shortest trip duration compared to the study areas.

Furthermore, the inter-individual variability was not high (for the maximum extent and the trip duration, the percentages of different individual pairs were 32.75% and 51.72% respectively), suggesting a relatively low spatial partitioning. These results suggest that grey seals travelled directly to their feeding areas. Previous telemetry tracking on grey seals in the North Sea showed that they tended to cross long distances directly to offshore areas, to feed on sandeels, on specific and known sandbanks, allowing them to avoid the competition with/or bycatch by the fisheries (Hammond et al., 1994;McConnell et al., 1999). This offshore behaviour was found in my results on spatial usage. However, contrarily to McConnell et al. (1999) my results showed that grey seals didn't select foraging areas over sandbanks. In this study, grey seals selected coarse-sediment (and not sandy areas), and avoided mud (as opposed to results from Aarts et al., 2008). This difference with the literature can be explained by the fact that grey seals performed straight offshore trip over coarse-sediment banks.

The distribution of the trip maximum extent from the Firth of Tay was not significantly different from the one in the Eastern English Channel. The values of trip maximum extent and trip durations were significantly higher than the Irish Continental Shelf, the Irish Sea and the Iroise Sea. This result of spatial usage suggests that grey seals travelled directly to known feeding areas in the Eastern English

Channel. This was supported by the relatively high inter-individual similarity of trip duration (only 38.5% of individual-pairs comparison were significantly different).

Furthermore, the combination of selection of the different habitat characteristics (sediments, distance from shore and current), seem to correspond to the mud bank found on the middle of the English Channel, linked to important tidal current speed.

Furthermore, recent grey seals diet analyses highlighted that in addition to flat fishes, they feed on herring that can be associated to these areas (Planque, unpublished data).

This can be explained by the migration of juvenile herring coming from the North Sea to the English Channel [START_REF] Alheit | Long-term climate forcing of European herring and sardine populations[END_REF]. This species can represent a rich energetic gain for grey seals. This foraging behaviour (i.e. doing straight distance to "offshore" feeding areas) is quite similar of the one found for the Firth of Tay. However, the grey seals' estimated number in the Eastern English Channel is relatively low compare to the UK (150 compare to 139,800. Grey seals do not breed yet in the Eastern English Channel, so all grey seals found in this area were born in other colonies. It is suggested that they come from colonies along the East British coast and from the Netherland (this was shown by tracking of grey seals tagged in the Baie de Somme).

These seals might have their routine foraging trips in the North Sea, feeding in offshore areas to avoid intra-specific competition, and continue to adopt this behaviour in the Eastern English Channel.

In France, grey seals are at their southern limit of their range, and the seal numbers are relatively low compared to the core population. The seals' spatial usage in this area was quite different from the Firth of Tay and the Eastern English Channel study areas, as grey seals performed trips with shorter maximum extent but longer duration. They selected foraging habitat close to their haulout site, over mixed sediments and with a minimum speed of tidal current. However, previous studies showed that grey seals in the Iroise Sea mainly feed on the wrasse (Ridoux et al., 2007). This type of prey does not represent a commercial fish species. A high prey density and a low intra and inter (with fisheries) specific interactions allowed to grey seals to select their habitat surrounding their haulout sites in this area. This fish species is mainly found in rocky areas, so I was expecting a selection of foraging habitat over rocky areas and not mixed sediments.

Grey seal tracks from the Iroise Sea showed seasonal movements towards Irish grey seal colonies on the continental shelf.

Regarding the spatial usage of grey seal in this area, their trip maximum extents were lower than the other sites, and they had intermediate trip durations compared to the other sites. Grey seals selected their foraging habitat at short distance from the last haulout and over rocky, mixed and coarse sediments. These results matched with those found by (Jessopp et al., 2013), who found that grey seal mainly performed their benthic dives on complex rock substrates. These facts agreed with the grey seals diet in this region (Sandeel, dragonets etc., Gosch et al., 2014)). Despite the number of seals in this colony (800 seals), the high biodiversity and the lack of interspecific competition with the fisheries (particularly with offshore whitefish fishery using trawling gear, Cronin et al., 2012), allow the seals to forage at short distances from their haulout sites compared to the North Sea, and to adopt a foraging behaviour closer to the one observed on grey seals in the Iroise Sea.

On the other side of Ireland, one grey seal colony occurs at Raven Point with around the same number of seals. The median of the trip maximum extent performed by the seals in the Irish Sea was higher than in the Iroise Sea or on the Continental Shelf, and lower than in the Firth of Tay or the Eastern English Channel. However, the median trip duration was the highest. Furthermore, the Irish Sea had one of the highest rates of inter-individual variability for the trip duration and trip maximum extents (50% and 59.61% of individual-pairs comparison significantly different). Similar numbers of seals occurs in Irish Sea and on Irish Continental shelf, however this difference in trip durations, trip maximum extents and in rate of inter-individual variability might come from the interaction with fisheries. Indeed, interactions between seals and net fisheries were observed in the Irish Sea. Furthermore, the characteristics of foraging habitat selected by grey seals in this area (i.e. positive influence of tidal current, selection of sandy, muddy and rocky habitat) seem to match with those of their preys (demersal gadoids, Trispopterus sp, and commercial species such as haddock, pollock saithe and whiting.

All these results highlighted the influence of habitat and prey availability, and the effect of intra and inter-specific interactions on the seals' behavioural plasticity reflected by the foraging habitat selection and spatial usage. Globally, there were some variations in foraging habitat selection between the different study areas. These variations might be linked to the habitat of prey found in the diet of grey seals in these different areas. However, the variability of spatial usage between and within study areas linked to the density of grey seals and the possible interactions with fisheries suggested that a certain degree of grey seal behavioural plasticity, resulting in a trade off between cost and benefits might be occur. Thanks to the degree of foraging behaviour plasticity, the adaptation of grey seals was highlighted by the degree of intra and inter-sites variability of trip characteristics, in addition to the results of habitat selection. For example in the North Sea, in the core grey seal population, seals tend to travel directly to offshore areas on sandbanks with a high quantity of prey, avoiding inter-specific competition (with fisheries) and probably intra specific competition (due to the high density of seals). However, this site had the lowest rate of inter-individual variability. The seals tagged in the Firth of Tay seem to have a similar behaviour, as they have the opportunity to forage over offshore bank with high quantity of sandeels.

This might be sufficient for them to forage over the same area, adopting a similar behaviour. On the opposite, grey seals in the Irish Sea adapt their behaviour depending on the influence of intra and inter-specific interactions (fisheries and seals) and habitat availability, by increasing the inter-individual spatial usage.

However, the results obtained on grey seals in the Eastern English Channel The results on the seal data used to answer to this question, showed quite distinct behaviours. Two of them showed a typical "inshore behaviour". It was the case for B37, which remained very coastal in the Iroise Sea and around the Cornwall. G04 also adopted this behaviour, being coastal in both the Eastern English Channel and in the Wadden Sea. On the opposite, G11 selected more "offshore" areas in the Eastern English Channel and in the North Sea, foraging directly on an offshore bank. B31, who spent time in the Iroise Sea and on the Irish Continental Shelf, changed its behaviour, selecting a foraging habitat close to its haulout sites in the Iroise Sea but selecting offshore foraging areas on the Irish Continental Shelf. With these results it is quite difficult to answer to the question as another factor than the individual behavioural plasticity seems to play a role in the process of foraging habitat selection. [START_REF] Twiss | An in-field experimental test of pinniped behavioral types[END_REF] have shown the individual personality might have an impact on the grey seals' behaviour (i.e. behavioural types). The mix between plasticity and personality was demonstrated with the great tit (Parus major, [START_REF] Dingemanse | Variation in personality and behavioural plasticity across four populations of the great tit Parus major[END_REF], particularly in the exploration of new environment, highlighting strong individual variabilities.

Furthermore the evolution of this behavioural trait will vary depending of the environmental conditions encountered by the animal. This might be particularly strong for grey seal that do not learn from their parents. These results suggest that the individual personality might have an impact on the behavioural plasticity.

To conclude, these results demonstrated that the foraging habitat selection and spatial usage of grey seals reflected the influence of behavioural plasticity subject to the pressure of habitat and prey availability, and interactions between organisms. The potential role of individual personality can also be suggested as a factor influencing individual grey seal foraging strategies.

CHAPTER 5

Photo: Yann Planque

HARBOUR SEAL -SPATIAL USAGE AND FORAGING HABITAT SELECTION

This chapter presents the results of spatial usage and foraging habitat selection by harbour seals. Tracking maps (Figure 2.6-3) showed that, in one study areas, harbour seals did not move from one area to another and performed only return trips, therefore I only present here foraging habitat selection by harbour seals at the colony scale.

Intra and inter-site spatial usage variability

Intra and inter-site spatial usage variability were explored on harbour seals for the different study areas with two metrics: trip duration and trip maximum extent. Trips shorter than 3h were deleted, as they were not considered as linked to foraging activities, but rather as short movements between consecutive haulout sites. As the hypotheses of normality and homoscedasticity were rejected (p>0.05), I used Kruskal-Wallis test. I also used the median to compare the trip maximum extent and trip duration between and within sites to avoid outlier effect.

Trip duration

Trip duration was significantly different between study areas (p<0.001, Table 5 ). The inter-site variability was higher than the intra-site one (Figure 5.1-1). The range of values of trip durations for seals tagged in the Inner Hebrides was larger than that for the other sites.

Trip maximum extent

Maximum extent was significantly different between all sites (p<0.05, Table 5.1.1-2).

Post-hoc tests were used to do pair-site comparisons. When modelling the foraging habitat selection, no colinearity was detected (Pearson coefficient <0.75) and one model was created including all the variables: the distance from the last haulout, the distance from shore, the bathymetry, the sediment types and the tidal current (Table 5.2-1). The deviance explained by the model was 62.3%.

Distance from the last haulout and the bathymetry accounted for most of the model deviance explained (67.20% and 26.20% respectively). The other variables: the tidal current, the sediment types and the distance from shore accounted together for less than 5% (2.90, 2 and 0.5% respectively, Figure 5.2-2, Table 5.2-1). In this area, harbour seals selected foraging area close to their haulout sites, with foraging habitat selection decreasing further 15-20 km from the last haulout. The bathymetry also had a negative influence, with a habitat selection decreasing when bathymetry gets deeper than 10 meters. In the bay, harbour seals selected an optimal tidal current speed around 0.3m/s. Harbour seals selected rocky and mixed sediments, avoiding mud and coarse sediments (the influence of the sand was not significant).

The predictions of habitat selection are presented • Baie des Veys A total of 62,136 of dives were identified as foraging dives. In the Baie des Veys, the main foraging area is located in the middle of the Bay (in front of the mouth of the estuary, Figure 5.2-1); while a secondary one (95% Kernel density) was highlighted along the coast, East of the bay.

During the data exploration of foraging habitat selection, I detected colinearity between the distance from shore and the bathymetry (Pearson coefficient>0.75). Then, two models were created: one excluding the bathymetry and the other one excluding the distance from shore. The optimal model selected was the one with the lowest AIC (Table 5.2-2), excluding the distance from shore. The model selected included the distance from the last haulout, the bathymetry, the sediment types and the tidal current ( 5.2-3). In the Baie des Veys, harbour seals selected foraging habitat close to their haulout site. They also selected their foraging habitat in shallow waters as shown by the decreasing probability in waters deeper than 20m. Harbour seals selected an optimal of tidal current speed of 0.5m/s. They selected rock, coarse and mixed sediments, avoiding sandy and muddy areas. While modelling foraging habitat selection in the Baie de Somme, colinearity was detected between the distance from the last haulout and the bathymetry and between the distance from shore and the bathymetry (Pearson coefficient >0.75). Consequently, two models were created: one excluding the distances from the last haulout and from shore and the other one excluding the bathymetry. The model including the bathymetry had the highest AIC and was rejected by the model selection. Thus, the optimal model included the distance from the last haulout, the distance from shore, the sediment types, and the tidal current (Table 5.2-4). The deviance explained by this model was 77.3%. The distance from shore explained most of the model deviance with 92.4%. The distance from the last haulout, sediment types, and the tidal current explained less than 8% (Figure 5.2-6, Table 5.2-5).

Harbour seals selected their foraging habitat close to the shore (mostly within 20 km,). The distance from the last haulout had a negative influence on the seals' habitat selection. This selection first decreased strongly up to 40 Km, remained stable from 40 to 60 km and then decreased again for distances longer than 60 Km away from the last haulout. They selected an optimal value of tidal current of 0.35 m/s. Harbour seals avoided coarse sediments, selecting mixed sediments, rocky and sandy areas.

In this area, the foraging habitat selection is mainly located around the Baie of Somme (Figure 5.2-7). Since no such dataset was available, it was impossible to include tidal current as an explanatory variable in the modelling of foraging habitat selection in this area.

Colinearity was detected between the distance from the last haulout and the bathymetry, as well as between the distance from shore and the bathymetry (Pearson coefficient>0.75). Two models were tested: one including the bathymetry, the sediment types and the other one including the distance from the last haulout, the distance from shore and the sediment types. The optimal model was the one with the lowest AIC (Table 5.2-6), excluding the bathymetry. This selected model included the distance from the last haulout, the distance from shore, and the sediment types. The deviance explained by the model was 60.1%. The distance from the last haulout and the distance from the shore explained 95% of the deviance (Figure 5.2-9, Table 5.2-7).

The selection decreased rapidly with the distance from the last haulout site, and distance from shore further than 8 Km.

Foraging habitat selection of harbour seals in the Kenmare River, is spread all over the bay (Figure 5.2-10) No colinearity between explanatory variables was detected (Pearson coefficient <0.75). All the covariates were therefore included in the optimal model of foraging areas selection: the distance from the last haulout, the distance form shore, the bathymetry, the sediment types and the tidal current. The deviance explained by the model was 75.1%. The distance from the last haulout accounted for 91% of the total deviance. The other variables: bathymetry, distance from shore, tidal current and sediment types accounted for less than 10% together (respectively: 2.2%, 0.3%, <0.1% and 1.9%), (Figure 5.2-12, Table 5.2-8).

Seals selected their foraging habitats at less than 150 Km from the last haulout, then the selection decreased rapidly for distance further to 150 Km. In the Inner Hebrides, harbour seals selected an optimal depth around 30 meters. There were more sediment types than in the other sites, indeed, two other sediment types were identified: "the shelf mound or pinnacle" and" the Shelf trough". In this area, harbour seals selected rocky and shelf mound areas, avoiding coarse and mixed sediments, muddy and sandy areas as well as shelf trough.

The foraging habitat selection was mainly selected around the islands of Inner Hebrides, particularly around Islay (Figure 5.2-13).

Discussion

The aim of this chapter was to study the spatial usage and foraging habitat selection of harbour seal at local scale, depending of the habitat availability and intra-specific interactions. The harbour seal population in Northeast Atlantic is spread into different colonies with contrasted dynamics. The core population is located around UK, whereas the species is at its southern limit of range in France. The species is known to feed on diverse fish species [START_REF] Hall | Seasonal variation in the diet of harbour seals in the south-western North Sea[END_REF].

I selected different study areas where the colony dynamics and habitat availability differed. Prey availability is also supposed to be different between the areas, but since it was not available in most of the study areas, I used different environmental variables as proxies supposed to represent the habitat of their preys (and thus prey availability).

The models' explained deviances were relatively high compared to other studies aiming at modelling the habitat selection of central place foragers. By comparison, Andersen et al. (2013) only explained from 5 to 21% % of the deviance in modelling foraging habitat selection of hooded seals. In the same way, [START_REF] Photopoulou | Spatial variation in maximum dive depth in gray seals in relation to foraging[END_REF].

only explained 37% of the deviance in modelling the habitat selection of grey seals; and [START_REF] Scales | Mesoscale fronts as foraging habitats: composite front mapping reveals oceanographic drivers of habitat use for a pelagic seabird[END_REF] Laurent estuary ( Lesage et al., 2004) seals remained near the coast (with distances shorter than 11 Km from the shore) and in shallow water areas (<50m deep). In the Moray Firth, the maximum distance travelled was 10 Km, diving at a maximum depth of 50m (Tollit et al., 1998b). Furthermore, harbour seals in San Francisco Bay mainly foraged near their primary haulout sites in a narrow deep channel ( [START_REF] Grigg | Foraging distribution of Pacific harbor seals (Phoca vitulina richardii) in a highly impacted estuary[END_REF].

In the Inner Hebrides (Northeast Scotland), harbour seals selected an optimal distance from shore of 40 Km and 20 m depth, foraging on rocky, shelf mound and shelf through. These results may correspond to the habitat of their preys in this area.

The analyses of the harbour seal' s diet around isles of Mull and Skye (Pierce and Santos 2008) showed that they mainly feed on gadoids species (representing an important part of their diet composition), including whiting (Merlangius merlangus), pelagic scad (Trachurus trachurus) and herring (Clupea harengus). These fish species are more pelagic and can be found along the sound of the fjords composing the Inner Hebrides. Thus, the foraging habitat selection modelled in this study could correspond to the sounds where seals can find these prey species. The colony size of harbour seals in this area is the highest of our study, representing around 15,200 individuals (SCOS, 2016), while in the other colonies the seal numbers lie within tens and a few hundreds of individuals. The colony size might explain the differences observed in the seals' spatial usage. Indeed, the median values of maximum trip extent and trip duration were higher in the Inner Hebrides than in the other sites. Furthermore, the range of values was larger than in the other sites. Consequently, due to the seal number in this area and the area habitat characteristics, I can interpret these results as spatial partitioning in response to the intra-specific interactions (limiting the competition).

The results of the boxplot suggest that these seals in this area may exploit a greater range of foraging behaviours that enable them to use more habitats and reduce competition.

On the other side of Scotland, nearly 230 harbour seals are found in the Firth of Tay, and this number is decreasing (SCOS, 2016). The habitat availability in the North Sea is different to the one in the Inner Hebrides (muddy-sandy areas compared to fjords). In this study area, the median values for trip duration and trip maximum extent were the lowest. Furthermore, the difference between individual range of values was a little bit higher than the other study areas. This was supported with the results of pairwise comparison with relatively high percentage of different pairs. By performing different ranges of trip maximum extents and trip durations, the individual may use the space differently to each other, reflecting spatial partitioning. The tidal current had a strong and negative influence on foraging habitat selection. Furthermore, the harbour seals mainly selected habitat at a distance of 40 Km from shore with 25 m depths. The influence of sediments here was too weak to interpret the results. However, harbour seals in the Moray Firth selected habitat with the same topographic characteristics as in the Firth of Tay ( Bailey et al., 2014), and over small grain size seabed (i.e. sand) corresponding to habitat of harbour seal main prey in the North Sea (i.e. Sandeel, [START_REF] Hall | Seasonal variation in the diet of harbour seals in the south-western North Sea[END_REF]. As the Moray Firth and the Firth of Tay share the same habitat characteristics, and despite the weak influence of sediment in my case, I supposed that the results can correspond to the habitat of harbour seal 's main prey. However, I found a difference in the influence of the tidal current compared to previous results obtained the Moray Firth ( Bailey et al., 2014).

In the Eastern English Channel, harbour seals are at their southern limit of their range in the Northeast Atlantic and spread into three colony: the Baie du Mont Saint Michel, the Baie des Veys and the Baie de Somme. Haulout numbers in the Baie de Somme reach 600 seals, versus 200 and 100 seals for the Baie des Veys and Baie de Somme respectively (Vincent et al., 2017). The results obtained for these three sites are relatively similar. However, I can notice small differences of the influence of the distance from the last haulout, the distance from shore and the bathymetry, which may be mainly due to the site configuration. The Baie du Mont Saint Michel represents a relatively enclosed area, inducing a weak influence of distance from shore and a rapid decrease of foraging habitat selection for depths deeper than 5m, as we know that harbour seals (cf tracking), moved out in the bay. On the contrary, the Baie des Veys and the Baie de Somme represent more "open " bays. In these two study areas, seals tend to follow the coastline. Harbour seals in the English Channel selected foraging habitat with an optimal tidal current speed (0.3m/s, 0.5 m/s, 0.35 m/s for the Baie du Mont Saint Michel, the Baie de Veys and the Baie de Somme respectively), over seabed of rock, sand and mixed sediments. These results just reflect the sites' sediment characteristics. In the Eastern English Channel, the tidal current drives the sediment distribution in the different bays along the French coast, resulting in same sediment configuration in the bays [START_REF] Larsonneur | The superficial sediments of the English Channel and its Western Approaches[END_REF]. These foraging habitat selections correspond to the diet composition of harbour seals in the three colonies. However, the diet composition is not the same between the three colonies. In the Baie de Somme, harbour seals mainly feed on flat fish, while they mainly feed on mullets in the two other sites (Spitz et al., 2015, Planque, unpublished data). The characteristics of the foraging habitat highlighted by the results correspond to the habitat of these preys.

Moreover this difference of diet composition among these three colonies can result from foraging traditions within harbour seal colonies; indeed parental investments in learning foraging techniques could occur during the lactation period (Spitz et al., 2015).

In addition to the habitat selection, the trip durations and trip maximum extents were similar between these three sites. Particularly for the median of maximum extent the Baie des Veys and the Baie de Somme, which were significantly different.

Furthermore the inter-individual variability was also relatively close when the 3 colonies are compared. However, median maximum extent for the Baie de Mont Saint Michel was higher than the other French bays, and its range's values was smaller. The difference in the range of values can be explained by the fact that harbour seals in the Baie des Veys and in the Baie de Somme performed trips along the coast, and the configuration of the western bay lead to restrict the spatial usage of the harbour seals.

The last study area in west coast of Ireland is the Kenmare estuary. Due to the size of the bay and the resolution of environmental data, it was a bit difficult to model the habitat selection of harbour seals in this area. Indeed, no tidal current data set was available and the only two types of sediment were available (mixed and sand), thus this variable had a very low influence on habitat selection. As the bathymetry was not included in the model because of colinearity, only distance from the last haulout and distance from the shore had an influence (here a negative one). These results can be explained by the central place foraging behaviour of the harbour seals. Although I could not characterize the habitat selection of harbour seals in this area, previous studies highlighted that the diet of the species was mainly composed by the sandeel, sole and Trisopterus species ( [START_REF] Kavanagh | Diet of the harbour seal (Phoca vitulina vitulina) in the west and south-west of Ireland[END_REF].

The influence of the habitat availability, and intra-specific interactions on the harbour seals' behavioural plasticity were highlighted with the differences of foraging habitat selection and spatial usage between the different study areas. Harbour seals are generally regarded as opportunistic feeders, choosing their prey according to their local abundance. Thus, in the Northeast Atlantic, diet composition for this species varied between areas. These differences in prey composition were highlighted by the foraging habitat selected in each area, as well as by the differences observed between each site. However, the characteristics of harbour seal as central place forager and benthic feeder are probably the main reasons for the influence of the distance from the last haulout and the bathymetry in the models.

I did not notice any influence of density dependence on the spatial usage between the Baie du Mont Saint Michel, Baie des Veys, Baie de Somme, Kenmare and Firth of Tay as there was not noticeable difference with the trip maximum extent and the trip duration. However, I noticed a difference of spatial usage between these sites and the Inner Hebrides (were the seal abundance is much larger than in the other sites) in largest range and highest values.

In this study harbour seals did not travel far away from the coast (this was also highlighted with the influence of bathymetry). However, considering their morphological capacities, they are able to travel much further and to dive deeper. In Monterey Bay (Pacific Ocean), the maximum diving depth for harbour seal (P. v.

richardii) was recorded at 481m [START_REF] Eguchi | Diving Behavior of the Pacific Harbor Seal (Phoca Vitulina Richardii) in Monterey Bay, California[END_REF]. On the Northeast continental shelf, available sea-bed depths do not exceed 200 m.

The prey availability might be sufficient to deal with the effect of density dependence and prey depletion to maintain harbour seals inshore areas. Furthermore, the maternal learning and the possible effect of predators in offshore area could also have an influence.

CONCLUSION

The objective of this PhD was to study the spatial usage and foraging habitat selection of two sympatric seal species, the grey and the harbour seals in the Northeast Atlantic. As the habitat availability and seal population dynamics are not the same among colonies, I divided the Northeast Atlantic into different parts. This division was performed in order to focus on the local foraging habitat selection of both species.

Innovative aspects of the PhD

In this PhD, I addressed two methodological aspects, which are not often taken into account in habitat modelling: the foraging activities and the local scale of the habitat selection modelling. In chapter 3, focusing on these methodological choices, I demonstrated the importance to focus on foraging activities to perform habitat modelling by using the vertical approach (according to the benthic foraging behaviour of seals). If previous studies have already linked the foraging activities to the habitat selection (on hooded seals for instance or racoon (Andersen et al., 2013;[START_REF] Byrne | Using first-passage time to link behaviour and habitat in foraging paths of a terrestrial predator, the racoon[END_REF] most published studies did not focus on this specific activity when they modelled habitat selection (Aarts et al., 2008;Huon et al., 2015;[START_REF] Land | Florida Panther Habitat Selection Analysis of Concurrent GPS and VHF Telemetry Data[END_REF]Wakefield et al., 2011).

Contrarily to terrestrial ecosystems, in the case of marine top predators, only few habitat selection studies have been conducted at a fine scale [START_REF] Cama | Living on predictability: modelling the density distribution of efficient foraging seabirds[END_REF][START_REF] Garaffo | Modeling habitat use for dusky dolphin and Commerson's dolphin in Patagonia[END_REF]Huon et al., 2015;[START_REF] Michelot | Seasonal variation in coastal marine habitat use by the European shag: Insights from fine scale habitat selection modeling and diet[END_REF], most of them being conducted on a large spatial scale in the open Ocean [START_REF] Hastie | Fine-scale habitat selection by coastal bottlenose dolphins: application of a new land-based video-montage technique[END_REF][START_REF] Stevens | Testing the utility of abiotic surrogates for marine habitat mapping at scales relevant to management[END_REF].

However, studying habitat selection at a fine scale is important to take into account the ecosystem complexity, particularly in estuarine areas (or the Iroise Sea for example in this PhD). This was mainly not done because of a lack of fine scale environmental dataset [START_REF] Stevens | Testing the utility of abiotic surrogates for marine habitat mapping at scales relevant to management[END_REF]. Furthermore, a lot of studies aiming at modelling habitat selection of marine fauna were performed at large scale (i.e. basin ocean). This was the case for different species travelling long distances, such as cetaceans or sea birds [START_REF] Lambert | Predicting Cetacean Habitats from Their Energetic Needs and the Distribution of Their Prey in Two Contrasted Tropical Regions[END_REF][START_REF] Panigada | Modelling habitat preferences for fin whales and striped dolphins in the Pelagos Sanctuary (Western Mediterranean Sea) with physiographic and remote sensing variables[END_REF][START_REF] Virgili | Predicting seasonal variations in coastal seabird habitats in the English Channel and the Bay of Biscay[END_REF].

However, dataset analysed in these studies were provided by aerial surveys. Datasets obtained from telemetry surveys allow studying the habitat selection at a finer scale.

Nonetheless, to take into account the population dynamics, it is important to work at local scale. This is particularly true for the central place forager species, such as pinnipeds. Their populations are spread into different colonies with potentially distinct population dynamics (core population versus limit ranges), local abundance, habitat and prey availabilities etc. Studying the foraging habitat selection and habitat use of grey and harbour seals in the Northeast Atlantic represented an interested application case. In fact, populations of both species are spread into different colonies with contrasted numbers, population dynamics (particularly between the core population and the range's limits), and differences of habitat and prey availabilities. I modelled the foraging habitat selection at a local scale, in order to take into account the biological processes (such as the behavioural variability), and applying the same protocol to all study areas. To our knowledge, this is the first time this approach is chosen, especially for these two species. The habitat selection was already modelled at a local scale in Scotland, for grey and harbour seals in the Farnes Isles, Isle of May, Abertay and in the Moray Firth respectively ( Aarts et al., 2008;Bailey et al., 2014). These studies were not carried out in a context of integration over a larger scale. The seals' spatial usage and habitat selection were studied at a global scale (all colonies pooled together) by the Sea mammal Research Unit (Jones et al., 2015), in order to answer governmental requests.

Studying habitat selection and spatial usage at the population scale or at the colony scale do not answer to the same questions. Management questions (i.e. habitat selection at global scale) differ to ecological questions (habitat selection at local scale), and we do not consider the same approach (i.e. management units in the case of the SMRU for example). Thus, as I wanted to study the foraging habitat selection of grey and harbour seals, taking into account ecological aspects (i.e. behavioural plasticity, density dependence, variability between colonies etc.), I chose to focus on the local foraging habitat selection. This was particularly the case for grey seals, by selecting return trips in specific areas, in order to study the behavioural plasticity. Furthermore, the comparison of results obtained at the local scale provided new knowledge about the ecology and strategies of habitat use by grey seals and harbour seals in the Northeast Atlantic.

Comparing habitat selection by grey and harbour seals

Chapter 4 and 5 focused on the foraging habitat selection and spatial usage of grey seal and harbour seals respectively. For both species, the results highlighted the importance of the distance from the last haulout in the deviance explained. Whether this was at the colony scale or at the individual scale (for both species), there was a strong influence of the distance from the last haulout on the seals' foraging habitat selection. In most cases, this variable explained more than 50% of the model's explained deviance. For grey seals, at the colony scale this parameter explained 44.54 % (Firth of Tay) to 76.16% of the deviance (in the Iroise Sea); at individual scale it explained 12.1% (G11 in the North Sea) to 96.1% of the models' deviance (for G04 in the Wadden Sea). For harbour seals, the distance from the last haulout explained from 6.54% (Baie de Somme) to 90.86% (in the Inner Hebrides) of the model deviance. In every case, the distance from the last haulout had a negative influence on the foraging habitat selection, which means that seals selected foraging areas close to their haulout As grey and harbour seals are central place foragers, they need to come back to their haulout site to rest, breed, moult, and restrain their activities close to their haulout sites particularly during the two last periods. As I deleted the data during the moulting and breeding periods, in order to focus specifically on foraging activities, this very strong influence of the distance from the last haulout in the habitat selection modelling was not expected. This was particularly unexpected for grey seals. As they are capital breeder, outside the breeding and moulting periods, they need to reconstitute their blubber reserves. [START_REF] Bowen | Seasonal and Geographic Variation in the Species Composition and Size of Prey Consumed by Grey Seals (Halichoerus grypus) on the Scotian Shelf[END_REF] have shown that the diet of grey seals varies between winter and summer. Furthermore, the energetic content of prey seems to be higher in offshore areas. This can motivate grey seals to forage in offshore areas, depending on their trade-off between costs and benefits (i.e. the cost of crossing longer distances is counterbalanced by the energetic gains of preys). For this, they can travel long distances (compared to harbour seals, McConnell et al., 1999) and are able to sleep under the water, overcoming the constraint to come back to their haulout sites to rest. Harbour seals are considered as closer to income breeders, as both males and females keep foraging during the breeding period [START_REF] Stephens | Capital breeding and income breeding: their meaning, measurement, and worth[END_REF]). Neverthless, studies on harbour seals have shown that they can cross long distance and dive in deeper waters ( [START_REF] Grigg | Foraging distribution of Pacific harbor seals (Phoca vitulina richardii) in a highly impacted estuary[END_REF] than in my different study areas.

Another hypothesis to explain the strong influence of the distance from the last haulout can be the effect of predation. Grey and harbour seals have some predators at sea, such as killer whales or sharks [START_REF] Brodie | Predation by Sharks on the Grey Seal (Halichoerus grypus) in Eastern Canada[END_REF][START_REF] Lucas | Shark-inflicted mortality on a population of harbour seals (Phoca vitulina) at Sable Island, Nova Scotia[END_REF]; [START_REF] Saulitis | Foraging Strategies of Sympatric Killer Whale (orcinus Orca) Populations in Prince William Sound, Alaska[END_REF]. In the northern North Sea (around the Shetlands), this was particularly the case for harbour seal as they were predated by killer whales [START_REF] Bolt | Occurrence of killer whales in Scottish inshore waters: temporal and spatial patterns relative to the distribution of declining harbour seal populations[END_REF]. However in most colonies, grey and harbour seals do not have predators. I suggest predation may not be the main reason for this frequent return to haulout.

Interactions with fisheries have been highlighted, and their possible influences on habitat use have been suggested (particularly for grey seals in the Firth of Tay and in the Irish Sea). Avoidance from fisheries could therefore be one factor influencing this behaviour.

Another potential explanation is the effect of density dependence. For grey seals, the site where the distance from the last haulout constituted the highest percentage in the deviance was the Iroise Sea (where the number of seal is low with high prey availability around the haulout sites), and on the opposite, the site where this factor had the lowest percentage was in the First of Tay, where the core population is located and where grey seals forage on sandbank on offshore sites. However, this hypothesis does not really work for harbour seals, as the site where the distance from last haulout explained the most the deviance was in the Inner Hebrides (at 90.86%) where number of harbour seals is higher than on the other sites.

One of the main differences between grey seals and harbour seals was the interindividual variability in habitat use. Globally this inter-individual variability was higher for grey seals than for harbour seals. I suggest that there is an influence of maternal learning present with harbour seals (Spitz et al., 2015). The absence of learning in grey seals leads them to have an exploratory behaviour higher than in harbour seals, learning their "routine" individually.

Grey seals and harbour seals live in sympatry and are known to share the same haulout sites at some places in the Northeast Atlantic and to feed partly on the same prey species (Thompson et al., 1996). However, little is known about their potential overlap or partitioning of habitat use and habitat selection.

In the Northeast Atlantic the grey seal and harbour seal population dynamics are contrasted. Globally, if the numbers of grey seals tend to increase in most colonies (SCOS, 2016; Vincent et al., 2017), it is not the case in every harbour seals colonies.

Several declines in counts of harbour seals are reported in different colonies, while some others remain stable or increase. These declines mainly occur in eastern Scotland (down by 75% between 1997 and 2015), in Orkney (down by 78% between 1997 and 2013), and in Shetland (30% between 2000 and 2009). On the opposite, harbour seal numbers in Western Scotland increased by 43% (between 2013 and 2015) and by 50% in the western Isles ( 2011), [START_REF] Scos | Scientific advice on matters related to management of seal population[END_REF]. During the last decades, some harbour seals colonies were strongly affected by the Phocid Distemper Virus (PDV) in the North Sea (Heide- Jorgensen et al. 1992), deeply affecting some colonies. The origins of the present declines are not well understood. Possible causes for this decrease may include direct mortality from vessel interaction [START_REF] Bexton | Unusual Mortality of Pinnipeds in the United Kingdom Associated with Helical (Corkscrew) Injuries of Anthropogenic Origin[END_REF], infectious disease (Hall et al., 2006;[START_REF] Harris | Evaluating the Influence of Epidemiological Parameters and Host Ecology on the Spread of Phocine Distemper Virus through Populations of Harbour Seals[END_REF], biotoxine exposure [START_REF] Hall | Evidence of domoic acid exposure in harbour seals from Scotland: A potential factor in the decline in abundance?[END_REF], as well as interactions with grey seals (SCOS, 2016). On the other side of the Atlantic, in Sable

Island, the number of harbour seals decreased sharply in the 1990's, which was suggested to be due to the presence of grey seals (Bowen et al., 2003). However, the predation by white shark was also mentioned as a potential explanation for this decreased abundance [START_REF] Ellis | Maternal effects on offspring traits from birththrough weaning in the harbour seal, Phoca vitulina[END_REF].

In Northeast Atlantic two kinds of interactions can occur between grey and harbour seals: predation and competition. Regarding the predation, several carcasses of harbour seals were found with specific injuries, first attributed to turbines [START_REF] Thompson | Report on recent seal mortalities in UK waters caused by extensive lacerations[END_REF]. However, grey seals were observed predating on harbour seals (e.g.

around the island of Helgoland, van [START_REF] Van Neer | Grey seal (Halichoerus grypus) predation on harbour seals (Phoca vitulina) on the island of Helgoland, Germany[END_REF]. Consequently, these injuries first attributed to turbines were finally attributed to this interaction between both species.

Furthermore, as the number of grey seals increased, the case of predations of grey seal on harbour seal, harbour porpoise [START_REF] Haelters | The Grey Seal (Halichoerus grypus) as a Predator of Harbour Porpoises (Phocoena phocoena)[END_REF] and grey seals pups (particularly from adult males) tended to increase. [START_REF] Morris | Toward an Ecological Synthesis: A Case for Habitat Selection[END_REF] considered the strategies of habitat selection as "one of the most convenient mechanisms for measuring competition for interacting species, and for community structures". In my PhD, I had two study areas where both species are found, the Firth of Tay and the Eastern English Channel. The historical records of these two harbour seal colonies are not the same. If the number of harbour seals in the Firth of Tay recently decrease (by 92% in 1997 and 2015, SCOS, 2016), it still increases in the Baie de Somme (by 14.6% per year between 1990 and 2015, Vincent et al., 2017).

Grey seals arrived recently in the Eastern English Channel. In both study areas, grey seals made longer trip than harbour seals. Median trips' maximum extents were 15 and 5 Km for grey and harbour seals respectively in the French area. In the Firth of Tay, medians of trips' maximum extent were 15 and 4 Km for grey and harbour seals respectively. The foraging areas and foraging habitat selection supported these differences in spatial usage. In both cases, harbour seals tended forage in inshore areas, while grey seals went further offshore. The two species also selected distinct sediment types. In the Eastern English Channel, grey seals selected muddy seabed and harbour seals sand, rock and mixed sediments. These differences in seabed selection are also supported by differences in diet. The latest results on the diet composition of both species in this area have shown some differences (the diet of grey seals was more diversified with herring, a prey not found in the harbour seals' diet, Planque et al. unpublished data). It is more difficult to highlight differences in sediment types selection between the two species in the Firth Of Tay, as this factor had a weak influence in the selection, particularly for harbour seals. These results cannot support the hypothesis of competition, as it seems that it should have a spatial partitioning (i.e.

these two species do not use the same areas).

I used two metrics to evaluate the influence of intra and interspecific interactions (following [START_REF] Blanchet | At-sea behaviour of three krill predators breeding at Bouvetøya-Antarctic fur seals, macaroni penguins and chinstrap penguins[END_REF]: the trip duration and the trip maximum extent. I also tested different methods to quantify the spatial overlap. [START_REF] Fieberg | Quantifying home-range overlap: the importance of the utilization distribution[END_REF]compared two statistic methods for measuring the degree of overlap between home ranges. The first one, the Utilisation Distribution Overlap Index (UDOI) is based on a generalization of Hulbert's (A978) E/E uniform statistic; the second one correspond to a general measure of similarity study, the Bhattacharyya's affinity [START_REF] Bhattacharyya | On a measure of divergence between two statistical populations defined by their probability distributions[END_REF]. Both methods result in indices varying from 0 (no overlap) to 1 (identical utilisation distribution). I applied the Bhattacharrya index (BA) on the 95% Kernel density of foraging dives locations to study the intra and inter-specific overlaps for the Eastern English Channel and the Firth of Tay. The results obtained are presented Figure 6.2-1.

In the Eastern English Channel, the median values of BA were 0.75 ± 0.14, 0.09 ± 0.02 and 0.05 ± 0.3 for harbour seals, grey seals, and both species respectively. In the Firth of Tay, the median values of BA were 0.38 ± 0.26, 0.04 ± 0.23 and 0.03 ± 0.14 for harbour seals, greys seals, and for both species respectively. The median value of BA for both sites is higher for harbour seal than grey seals, indicating a higher degree of overlap for harbour seals, particularly for harbour seals in the Eastern English Channel (indicating strong overlaps between individuals). This difference between grey and harbour seals might be explained by the degree of explanatory behaviour of grey seals, which is higher (cf. this chapter, above). The value of BA for interspecific overlap is relatively low for both sites (<0.01), indicating a low degree of spatial overlap. Globally the degree of spatial overlap is lower for the Scottish colonies compared to the French ones (i.e. values of BA lower). I suggest that this is due to the effect of density dependence, as the number of seals in Firth of Tay is higher, and seals have to use more different areas to find prey. Thus, with these results, I suggest a low degree of inter-specific overlap, and confirm the results obtained previously of foraging habitat selection modelling. Jones et al. (2015) and Sharples et al. (2012) also suggested a potential spatial partitioning, with grey seals using predominantly offshore areas and harbour seals inshore areas. Globally, both species don't share the same foraging areas.

The spatial partitioning can be linked to the theory of ghost of competition [START_REF] Rosenzweig | Habitat selection as a source of biological diversity[END_REF]. If the habitat segregation between species comes from competition; at the equilibrium the species can co-exist without entering in competition by living in separate habitats. In case of distinct habitat selection, each species occupies a separate habitat at equilibrium. When interspecific competitors resolve their co-existence by habitat segregation, their competition might be invisible ("ghost") because the species occupy separate habitats. However, when the densities of each species become too important to stay in separate habitat, both species start to mix their habitat, then entering in competition. [START_REF] Morris | Measuring the ghost of competition: Insights from density-dependent habitat selection on the co-existence and dynamics of lemmings[END_REF] experimented this theory with two species of lemmings. When the density of each species was high, both species occupied the same habitats. However, when the densities decreased, each species moved to a specific region representing a separate habitat (i.e. ghost of competition).

This phenomenon of ghost of competition can be illustrated in the Eastern English Channel. In this area, harbour seals were tagged while the grey seal abundance was still very low (2008). The results showed that grey seals used different foraging areas, not previously occupied by harbour seals. Since then, the abundance of both species has increased (Vincent et al., 2017). Thus, it will be interesting to tag again harbour seals in this area, to see the evolution of the foraging habitat selection and habitat use after the increase of grey seal local abundance. Regarding the Firth of Tay, the results obtained did not confirm the inter-specific competition between two species. Thus the diminution of the number of harbour seals might be due to a mix of different factors.

Perspectives

The results of this PhD bring new knowledge on the ecology of grey and harbour seals in Northeast Atlantic, particularly with the foraging habitat selection and spatial usage. However, some questions remain. Grey seals' tracks have shown that they crossed long distances between colonies, then spending a certain amount of time around one colony. This raised the question of the degree of adaptation of grey seals to local habitat when one individual switched from one colony to the next. These results at the individual scale suggested that the foraging habitat selection varies between individuals. As I could only carry out these analyses on 4 seals (for which enough telemetry data was available in two distinct regions), my sample size was not big enough to distinguish the effects of sex and age. However, grey seal behaviour varies between ages, sex, and morphology (Beck et al., 2003). Thus, it should be interesting to continue this work at the individual scale by adding more individuals into the dataset.

In the case of interspecific interactions, it could also be interesting to track more harbour seals from the Baie de Somme, as the number of harbour seals is increasing, and they were tagged before the arrival of grey seals. This would allow us to see if the harbour seals in this areas changed their foraging habitat selection and spatial usage. I only worked on one part of Northeast Atlantic, taking only into account the west part of the North Sea. However, adding the foraging habitat selection of both species of the other sides of the North Sea, such as in the Wadden Sea for example [START_REF] Brasseur | Habitat preferences of harbour seals in the Dutch coastal area: analysis and estimate of effects of offshore wind farms[END_REF], would allow us to integrate more information on the ecology of both species at first local and then at global scale.

The establishment of Marine Protected Areas (MPAs) is an important tool for the conservation of marine biodiversity. By aiming to protect species' habitats and regulating anthropogenic activities where necessary [START_REF] Hooker | Marine reserves as a tool for ecosystem-based management: the potential importance of megafauna[END_REF], the designation of MPAs is a common approach for the management and conservation of marine mammals ( [START_REF] Gormley | First evidence that marine protected areas can work for marine mammals[END_REF] A combination of developments in satellite telemetry and statistical methods have allowed the study of spatial use to predict habitat selection of marine mammals to aid space planning (Cañadas et al. 2005, Bailey and[START_REF] Bailey | Using marine mammal habitat modelling to identify priority conservation zones within a marine protected area[END_REF]. Generally, the distribution of use by a population or an individual is shaped by the spatial distribution of the resources required by the animals and their selection of these resources. Johnson (1980) defines selection "as the process by which an animal actually chooses a resource or habitat." Different habitats are not equally available, and the existence of habitat selection is signified by habitat use greater than would be expected, proportional to the availability. Available distribution tells us in what proportion the different environmental covariates occur in the set of all available resource units. In quantifying habitat selection, we can identify habitat that is particularly highly utilized. For central place foragers, such as pinnipeds and seabirds, the need to periodically return to their terrestrial haulouts or colonies also shapes their observed use (Orians and Pearson 1979). Such accessibility constraints need to be considered when estimating habitat availability (Matthiopoulos 2003).

Gray seals live in temperate and sub-polar waters. Three distinct stocks are identified in the northwest Atlantic, northeast Atlantic, and Baltic Sea. In the northeast Atlantic, the gray seal core population is located around the United Kingdom, with an estimated number of 104,200 seals (Special Committee on Seals [SCOS] 2013). In that region, gray seals move between colonies throughout their life cycle ( McConnell et al. 1992, Vincent et al. 2005). In France, gray seals are at the southern-most limit of their range with lower abundances (a few hundred individuals) and there are only 2 breeding colonies located in Brittany, west France (Vincent et al. 2005). The study colony is located in the Iroise Sea (Fig. 1). In 2000, 98 (95% CI: 75-175) seals were estimated to use the area seasonally, and this number has increased by around 7% per year over the last 2 decades (Gerondeau et al. 2007). Surveys of haulout counts highlighted that the peak number of gray seals in this area occurs during the molting period from January to March and the abundance is lowest during the breeding season from October to December ( Vincent et al. 2005). Pup production in the area is very low, at generally less than 10 pups per year. Females are mainly observed in the Iroise Sea during the summer when they build up their energy reserves for the breeding season (Gerondeau et al. 2007). Telemetry data show that some seals move to England, Wales, and the Channel Islands to breed (Vincent et al. 2005). Gray seals are considered opportunistic and generalist predators, consuming a variety of demersal and pelagic fish ( Austin et al. 2006). However, they show seasonal and regional diet variation (Hammond et al. 1994, McConnell et al. 1999, Breed et al. 2006, Beck et al. 2007). In the Iroise sea colony, fecal analyses revealed that the 3 main gray seal prey species are wrasse (mostly Labrus bergylta; 50.6%), conger eel (Conger conger; 20.7%), and sea bass (Dicentrarchus labrax; 11.9%; Ridoux et al. 2007). These fish species are abundant in the kelp forest (Laminaria spp.) of the Iroise Sea. An SAC was created in 2002 in this area, and encompassed within a larger marine natural park in 2007.

Annex II of the European Habitats Directive and French legislation of marine national parks requires the seal colony in the park to be monitored. A number of management actions are directed toward human activities that are known to have direct or indirect negative effects on the seal population. Recent management plans in the marine park focus on seals' at-sea habitat selection because direct interactions with fisheries are expected (e.g., bycatch interaction with seal depredation and resource competition). The objective of this study was to characterize habitat selection of gray seals in the marine park of the Iroise Sea in France to help environmental managers identify areas where there may be overlap between habitat selection and anthropogenic activities.

STUDY AREA

The marine park of the Iroise Sea covers an area of 3,550 km 2 at the western point of Brittany, France (Fig. 1B). It includes an archipelago of small islands characterized by shallow areas and numerous tidal reefs. Seals haul out (rest on land) on the tidal rocks year-round, whereas islands permanently emerged are only used during breeding (Oct-Dec) and molting (Jan-Mar) periods. Human activities taking place in this area include commercial fishing, sediment extraction, kelp forest exploitation, and recreational activities such as sailing, angling, seal watching, scuba diving, and jet-skiing; these are all monitored and regulated, particularly to protect the marine park wildlife, including gray seals. We chose a rectangular study area encompassing seal locations (47.63-48.828N, 5.37-4.228W) within which environmental data were available (Fig. 2).

METHODS

Telemetry data from 19 sub-adult and adult seals tracked with global positioning system (GPS) satellites and transmitted via the global system for mobile communications (GSM) were used to model habitat selection within the marine park. We used generalized linear models (GLMs) to model seal habitat selection to understand the importance of both continuous and categorical predictors. Previous studies have shown that gray seal space use at-sea is influenced by a wide range of environmental covariates such as depth (Sj€ oberg and Ball 2000, Aarts et al. 2008), sediment type ( Aarts et al. 2008), and distance to haulout (resting on land; Thompson et al. 1996). We followed Matthiopoulos (2003[START_REF] Matthiopoulos | How to be a quantitative ecologist: the 'a to r' of green mathematics and statistics[END_REF], Aarts et al. (2008), and[START_REF] Hirzel | Habitat suitability modelling and niche theory[END_REF][START_REF] Hirzel | Habitat suitability modelling and niche theory[END_REF] in defining habitat as a contiguous region in environmental space, whose attributes represent biotic and abiotic environmental variables to study the habitat preferences of gray seals from the marine park.

Data Collection

Telemetry tracks.-Individual movements of seals were tracked outside the breeding season using Fastloc GPS/GSM tags (Series 900 GPS Fastloc; Wildtrack Telemetry Systems Ltd., Leeds, United Kingdom) produced by the Sea Mammal Research Unit (SMRU) at the University of St. Andrews ( McConnell et al. 2004). Seals were captured under licences N810/102, 11/873, and 13/422 delivered by the French Ministry of Environment. Seals were caught in the water surrounding haulout sites with hoopnets, then anesthetized with Zoletil (Vibrac, Carros, France) at an intramuscular dose rate of 0.5 mg/kg body weight (Baker et al. 1990), and weighed. Tags were glued to the fur of the neck using a quick setting epoxy resin. The tracking period, therefore, extended from the beginning of the summer (Jun) until the following molt period (Jan-Feb; Table 1).

The tags used a wet/dry sensor to determine whether the seal was hauled out, swimming at the surface, or diving (below a chosen depth threshold, here 1.2 m). A haulout event started when the sensor was continuously dry for more than 10 minutes and ended when it was continuously wet for 40 seconds. We filtered locations to eliminate those that led to unrealistic swim speeds (>2 m/s; McConnell et al. 1992). On average, tags transmitted 42.3 AE 19.3 filtered GPS locations per day. To reduce sampling bias (between areas where the seals spend more time diving or out of the water), we interpolated all GPS locations every 20 minutes using straight-line interpolation.

Return trips and control points.-We defined 2 categories of trips from the telemetry data: travel and return trips (sensu McConnell et al. 1999) based on whether the departure and destination haulout was the same (return trip) or different (travel trip; Fig. 1). We modeled habitat selection of seals in or around the marine park by including tracks of return trips from haulout sites located within the marine park (no return trip from other haulout sites extended to this marine area). We used only at-sea locations to study marine habitat selection by discarding all locations obtained while the wet/ dry sensor showed the seal was hauled out. Telemetry data can be spatial and temporally autocorrelated ( Aarts et al. 2008), which can be problematic for the model selection because autocorrelation can artificially inflate the degrees of freedom in data, resulting in over-parameterized models. We addressed this serial autocorrelation by systematically sampling the data ( Lesage et al. 2004, Aarts et al. 2008), selecting 1 in every 10 locations for each individual (corresponding to 1 location every 200 min). Habitat selection analyses investigating both continuous and categorical predictors typically infer selection of habitats by comparing measured environmental characteristics of used points, represented by telemetry locations, to those of random points that represent available habitats (Johnson et al. 2006, Lele and Keim 2006, Aarts et al. 2008). This represents the useavailability design (Keating and Cherry 2004). The random points were created locally within the study area using the package sp with R v 2.14.2 (R Core Team 2012). Typically, the number of random points varies between 1 and 5 times the number of telemetry locations (Pearce and Boyce 2005, Wakefield et al. 2011). In this study, we chose the number of random points to be twice the number of telemetry locations (Fig. 2) to balance the restricted size of the study area with an appropriate density of points.

Environmental data.-Based on previous studies of seal habitat preference (Aarts et al. 2008, Bailey et al. 2014), we used 3 environmental variables to identify habitat preference (see Figs. S1,S2,S3, available online at www.wildlifejournals. org). We obtained bathymetry data from the Service Hydrographique et Oc eanographique de la Marine (SHOM) database, at a spatial resolution of 100 m. Tidal current data were available hourly from the Previmer database (Lecornu and De Roeck 2009). These data were obtained from the MARS2D Iroise model, one of the northeast Atlantic models, at a spatial resolution of 250 m. We used a 4-month tidal current mean, corresponding to the seasonal tracking duration (Jun-Sep). This allowed the quantification of the tidal current strength in space irrespective of instant tidal phases (ebb, slack, or rising tide). Tidal current data had the least spatial extent and, therefore, the study area was limited by this variable (see Study Area). Sediment data were obtained from the Mapping European Seabed Habitat (MESH) project and included outputs of the MESH_EUNI model, which predicted seabed habitat types as polygons. The MESH_EUNI model was based on different environmental layers with spatial resolutions from 100 m to 250 m. Sediment types were based on a simplified Folk classification system (Folk 1954) and limited to the most dominant types: rock, mud, sand, gravel, coarse (gravel and mud), and mixed (gravel and sand) sediments (Fig. S3). We calculated distance to shore as the straight-line distance to the closest point along the coast or the archipelago using the ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA) Nearest function. We used distance to haulout, defined as the geodesic distance between each location and the last haulout site used by the seal, to describe accessibility to the environment ( Aarts et al. 2008). We created a 250-m-resolution raster to calculate the distance from an animal's location to their associated haulout site.

Data Analysis

We used variance inflation factors (VIF) to assess multicollinearity between the environmental variables. Multicollinearity is considered to be important when the VIF value is higher than 10 ( [START_REF] Kutner | Applied linear statistical models[END_REF]). If 2 variables had VIF values higher than 10, we created 2 distinctive models, each including 1 of those 2 variables. We fitted generalized linear models (GLMs) in R (R Core Team 2012) to the data using a binomial family argument with a logit link function to estimate the parameters of an inverselogit selection model based on seal locations and random points (Johnson et al. 2006). On average, the selection of return trips gave 8,477 AE 4,412 locations per seal (Table 1), although we did not include intra-individual variability in the analysis. We used telemetry locations and random points as the response variable, taking the values 1 and 0, respectively. We added quadratic terms to the model for bathymetry and tidal current covariates because we expected the response to these terms to be non-monotonic. We treated sediment type as a factor variable, using coarse-sediment as the reference level. We selected the best model based on the lowest Akaike's Information Criterion (AIC; Akaike 1973) value and Table 1. Details of gray seals fitted with global positioning system-global system for mobile communications (GPS-GSM) tags in the Iroise Sea marine park and tracking duration. This table only shows seal data included in the present study; 2 more seals did not spend enough time in the study area to be included. considered models with an AIC difference less than 2 to be equivalent [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]. We calculated the deviance explained by the selected model and calculated the part explained by each covariate with the prediction function of the GLM, providing an index of the importance of each covariate in the chosen model. We plotted predicted values from the selected model using ArcGIS to show areas of habitat selection. We created a regular prediction grid of 10 À3 degrees resolution covering the spatial extent of the analysis.

Seal

RESULTS

We caught and tagged 21 seals in the marine park between 2010 and 2013, of which 19 were included in the analyses (Table 1). Tracking duration for each seal ranged from 15 to 236 days (159 AE 56 on average), transmitting a total of 142,567 speed-filtered GPS locations. From these, we calculated 326,054 interpolated locations. Sixty-seven percent of these locations were return trips from the study area. Of the 218,456 return-trip locations, 128,882 were at-sea locations and were kept in the analysis.

We did not detect multi-collinearity between covariates; all VIF values were lower than 3. Model selection resulted in 2 models with an AIC difference of less than 2. Both models explained 76% of the deviance in the data so we considered the performance of these models equivalent. We selected the most parsimonious model, with only the quadratic term for current being dropped (Table 2). The proportion of deviance explained by the covariates was distance to haulout (47.2%), bathymetry (21.8%), quadratic term of bathymetry (17.7%), distance to shore (6.6%), tidal current (5.0%), and sediment type (1.7%). Habitat selection declined with increasing distance to haulout (Fig. 3A, Table 3). The predicted probability of occurrence was 0.86 in areas located within 200 m of the haulout sites, decreasing to 0.02 for distances farther than 40 km. A bimodal pattern in selection was evident with higher selection for waters shallower than 50 m and deeper than 150 m (Fig. 3B). Seals selected areas that were a short distance from the shore (Fig. 3C). Tidal current had a negative influence on the habitat with selection values declining from 0.008 m/s for a tidal current speed lower than 0.031 m/s to 0.003 m/s for a tidal current speed higher than 2 m/s (Fig. 3D). Gray seals selected rock, mixed, and coarse sediments as opposed to mud. The presence of sand did not have any influence (Table 3).

Habitat selection was highest in shallow waters of the marine park archipelago around tidal areas where gray seals currently haul out, and in the bay of Brest along the northern mainland coast where rocky areas are found (Fig. 4). Low habitat selection was observed outside the marine park boundaries.

DISCUSSION

This is the first study of gray seal habitat selection in France, at the southernmost limit of the species' European range. Our study focused on the marine park of the Iroise Sea where the number of seals has increased during the last 20 years at a rate of 7% per annum (Gerondeau et al. 2007). We described current gray seal habitat selection at a colony level to inform local managers on spatial planning as they assess overlap with commercial and leisure activities.

Distance to haulout accounted for the largest part of the deviance explained in the model (47.2%). Seals selected areas located within 40 km from the last haulout site used. Almost all track points were located within this distance to haulout site and the tracked individuals spent 67% of their time within the boundaries of the marine park. Gray seals are central place foragers and spend the majority of their time close to the haulout sites ( McConnell et al. 1999). As part of the core North Sea gray seal population, at Abertay and the Farne Islands, Aarts et al. (2008) also describe a negative influence of the distance to haulout on the seals' habitat preference with a decrease after tens of kilometers from haulout, although seals moved farther offshore than in our study (hundreds vs. tens of km). McConnell et al. (1999) calculated that on average 43% of all seals' time from the same colonies was spent within 10 km of the haulout sites. Gray seals in the Baltic Sea also spend most of their time within the vicinity of their haulout site (Sj€ oberg and Ball 2000), with a preference for short distances to haulout (10-15 km).

There is a small colony of about a hundred gray seals in the marine park compared to 104,200 in the North Sea (SCOS 2013) and 350,000 in the northwest Atlantic (Thomas et al. 2011). The Iroise sea is home to 127 fish species, including sea wrasse, the main component of seal diet around France (Ridoux et al. 2007). This represents almost all species found along the French coast of the Atlantic Ocean and the English Channel [START_REF] Raffin | Bases biologiques et ecologiques de la conservation du milieu marin en mer d'Iroise[END_REF] and is, therefore, often described as an area of strong biodiversity [START_REF] Hily | Macrobenthic biodiversity in intertidal habitats of the Iroise Biosphere Reserve (Brittany, France)[END_REF]. The oceanography in the Iroise Sea is a complex system. This is a shallow area with high hydrodynamics [START_REF] Muller | Estimating the Lagrangian residual circulation in the Iroise Sea[END_REF]) because of topographical irregularities and the presence of strong tidal currents. These currents create thermal fronts Table 2. The 3 candidate models of seal habitat selection (use_availability_selection) for gray seals in the Iroise Sea marine park, 2010-2013 with Akaike's Information Criterion (AIC) values and deviance explained. We considered the model with an asterisk the most parsimonious among the 2 competing models (DAIC < 2).

Models

AIC DAIC Deviance explained (%)

à Use_availability_selection $ distance to haulout þ bathymetry þ (bathymetry) 2 þ distance from shore þ factor (sediments) þ current 8,723.0 1.7 76 Use_availability_selection $ distance to haulout þ bathymetry þ (bathymetry) 2 þ distance from shore þ factor (sediments) þ current þ (current) 2 8,721.3 0.0 76 Use_availability selection $ distance to haulout þ bathymetry þ distance from shore þ factor (sediments) þ current 8,926.2 204.9
with high biological production [START_REF] Birrien | Nutrients and primary production in a shallow stratified ecosystem in the Iroise Sea[END_REF][START_REF] Muller | Estimating the Lagrangian residual circulation in the Iroise Sea[END_REF]. The Iroise Sea is used by a number of migratory top predators (basking sharks [Cetorhinus maximus], long finned-pilot whales [Globicephala melas]; Hily and Gl emarec 1999). However, no published information is available regarding the seasonal or annual fluctuations of the abundance of non-exploited fish species, including wrasse. Still, potentially high prey availability combined with low seal density (compared to the core population in the United Kingdom) may lead to a low degree of intraspecific competition in the marine park, allowing the seals to forage efficiently close to their haulout sites. High prey availability would explain differences in selection (notably selection for foraging near shore) between seals around the United Kingdom and France. We found a bimodal response of the seal's habitat selection for bathymetry, accounting for 39.5% of the deviance explained. Due to the seals' selection of at-sea areas located within 40 km of their haulout sites, they also showed a propensity to stay in depths shallower than 50 m, reflecting the bathymetry available in the study area. Gray seals are described as benthic feeders (Thompson et al. 1991) and show varying depth preferences. In the northeast Atlantic, they typically have dive depths between 10 m and 50 m ( Tollit et al. 1998) and up to 80 m ( Tollit et al. 1998, Aarts et al. 2008). In the northwest Atlantic, they dive in deeper waters on the Canadian continental shelf (Breed et al. 2006). The depths selected by gray seals in the Iroise Sea agreed with those of other gray seal colonies in the northeast Atlantic. However we also highlighted that seals selected water deeper than 100 m in this study. Most seals' locations within the marine park were located in the intertidal area or in the shallow water around the haulout sites, but track points from 2 individuals were also located above a deeper canyon located west of the archipelago and the island of Ushant, influencing the model output.

Distance to shore accounted for 6.6% of the deviance explained and had a negative influence on the habitat selection. Distance to shore was included in the model in addition to the distance to haulout sites because in the Iroise Sea, seals can use the proximity of the shore to take advantage of the high productivity created by thermal fronts [START_REF] Muller | Estimating the Lagrangian residual circulation in the Iroise Sea[END_REF].

Our results showed a negative influence of tidal currents, accounting for 5.0% of the deviance explained. This contrasts with a study of harbor seals (Phoca vitulina; Zamon 2001Zamon , 2003) ) using telemetry data, which showed an aggregation of seals near areas with topographic relief and strong current, highlighting foraging site fidelity. Currents may allow greater and more predictable prey availability for predators. We, therefore, expected a positive influence of the tidal current.

Sediment type explained 1.7% of the deviance. The sediment types most selected by gray seals were rock, mixed, and coarse sediments, located around haulout sites within the kelp forest. [START_REF] Hily | Macrobenthic biodiversity in intertidal habitats of the Iroise Biosphere Reserve (Brittany, France)[END_REF] showed a high prey biodiversity around boulders located within kelp and a lower biodiversity on sediment types of mud or sand. Moreover, wrasse, the main gray seal prey in the Iroise Sea (Ridoux et al. 2007), are located in rocky areas. A main component of gray seal diet in the North Sea are sandeels (Ammodytes marinus; Ammodytes tobianus; Prime and Hammond 1990, Hammond et al. 1994, Brown et al. 2012) that are primarily found on sandy sediments; in this core area, gray seals show an avoidance for mud (Boulcott et al. 2007, Aarts et al. 2008).

Several studies on gray and harbor seal ecology using telemetry data to characterize space use and habitat selection have been subject to small sample sizes of tagged individuals and, therefore, vulnerable to potentially unbalanced sampling ( Lesage et al. 2004, Cronin et al. 2013). Even studies that rely on large sample sizes (e.g., [START_REF] Matthiopoulos | Using satellite telemetry and aerial counts to estimate space use by grey seals around the British Isles[END_REF]) manage to observe only a small proportion (e.g., <0.001%) of the population of individuals for a small part of their lives (e.g., <10 months), and the structure of the sample varies by age and sex depending on the ease of capture and tagging. In these large data sets, such issues of representativeness can be addressed by the use of mixed effects models [START_REF] Aarts | Comparative interpretation of count, presence-absence and point methods for species distribution models[END_REF], which partition the variability in the data according to the type of individual. Assuming that the age and sex structure of a population is known, the predictions of such mixed effects models can be appropriately reweighted to generate a more representative map of space use. However, our sample size was too small to allow even simple levels of disaggregation to take place. Because of the small size of the colony, our sample size represented a relatively large proportion of the population (around 20%). Therefore, we assumed that our sample of animals was representative of the underlying population and we did not account for individual variability. Betweenindividual variation can arise. For example, we observed that 2 seals selected deep areas, probably causing the bimodal distribution of the selected bathymetry in the model. For studies such as ours where animals had different tag durations, the number of locations each individual made to the analysis was dissimilar. This could have resulted in biased sampling, meaning the estimated variation in habitat selection may have been underestimated. Therefore, our conclusions regarding habitat selection need to be interpreted with care. It may, therefore, be useful to address the between-individual variability by introducing a random effect for individual seals.

MANAGEMENT IMPLICATIONS

The marine park of the Iroise Sea was the first one to be set up in France. This is the first study of gray seal habitat selection within this marine park, and in France as a whole.

We identified that gray seals selected habitat close to the shore, and particularly close to their haulout site. They also selected shallow bathymetry and rock sediment. This habitat corresponds to an environment with high biological productivity, including high availability of their main prey, wrasse. Maintaining such habitats within the marine park would, therefore, benefit gray seals and help maintain "favorable conservation status," a requirement for Annex II species under the European Habitats Directive. The gray seals in the park can potentially be negatively affected by a number of direct and indirect human activities. A number of recreational activities, such as seal watching, snorkeling, and spear fishing occur around the gray seal haulout sites in the Iroise Sea and are known to lead to increased interactions between visitors and seals. Commercial activities are also permitted in the marine park. The largest impact from these is expected to come from the indirect and direct interactions of fishery activities with seals. Incidental bycatch have been reported, particularly in gillnet fisheries, killing mostly juvenile seals (C. Vincent, Universit e de La Rochelle, personal observation). Fishermen in the marine park are increasingly reporting depredation by seals (seals stealing fish in the nets leading to commercial loss). Moreover, gray seal habitat can be deteriorated by sediment extraction and kelp harvesting. The results of our study can be used for spatial planning in the marine park when managing human activities that have direct or indirect impacts on the gray seal colony, i.e., having direct interactions with this species or contributing to habitat deterioration.

Introduction

Understanding how wild marine predators use their environment to acquire food is central in understanding their ecology and implementing conservation and management regulations (e.g. [START_REF] Costa | The Secret Life of Marine Mammals Novel Tools for Studying Their Behavior and Biology at Sea[END_REF][START_REF] Croll | An integrated approch to the foraging ecology of marine birds and mammals[END_REF]McConnell et al., 1999;[START_REF] Augé | Importance of studying foraging site fidelity for spatial conservation measures in a mobile predator[END_REF]Russell et al., 2013;Vincent et al., 2016). It also provides key elements to improve knowledge on ecosystems functioning as these animals are in upper trophic levels of their environment [START_REF] Bowen | Role of marine mammals in aquatic ecosystems[END_REF][START_REF] Hindell | Ecosystem monitoring: are seals a potential tool for monitoring change in marine systems?[END_REF][START_REF] Camphuysen | Top Predators in Marine Ecosystems: Their Role in Monitoring and Management[END_REF][START_REF] Frederiksen | From plankton to top predators: bottom-up control of a marine food web across four trophic levels[END_REF][START_REF] Heithaus | Predicting ecological consequences of marine top predator declines[END_REF]. Studies on diving marine top predators present a specific challenge because these animals evolve in a three-dimensional environment, therefore it is almost impossible to directly observe their foraging behaviour. In the last three decades an increasing number of studies on marine top predators used bio-logging devices to acquire high-frequency location and behavioural data, allowing the assessment of their behaviour including foraging at sea (e.g. [START_REF] Cooke | Biotelemetry: a mechanistic approach to ecology[END_REF][START_REF] Ropert-Coudert | Diving into the world of biologging[END_REF][START_REF] Carter | Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds[END_REF].

Because it is complicated to obtain ground-truth information on their actual prey captures, most of the studies have focussed on ways to characterize a likely foraging behaviour by using indirect clues [START_REF] Schreer | Classification of Weddell Seal Diving Behavior[END_REF]Fauchald and Tveraa, 2003;Fedak et al., 2001;[START_REF] Robinson | A comparison of indirect measures of feeding behaviour based on ARGOS tracking data[END_REF]Bailleul et al., 2008;[START_REF] Thums | A validated approach for supervised dive classification in diving vertebrates[END_REF][START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF][START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF]. They consequently had to make assumptions about the predators' behaviour to interpret them as foraging. By using telemetry data, they analysed movements performed by diving predators in the horizontal and/or vertical dimensions, and tried to discriminate them into different types of behaviours including foraging. Most analyses however were performed with tracking data (location data) in either the horizontal dimension (e.g. [START_REF] Bovet | Spatial analysis of animals' movements using a correlated random walk model[END_REF][START_REF] Thompson | Summer Foraging Activity and Movements of Radio-Tagged Common Seals (Phoca vitulina. L.) in the Moray Firth, Scotland[END_REF]McConnell et al., 1999;Fauchald and Tveraa, 2003;[START_REF] Tremblay | Fractal landscape method: an alternative approach to measuring area-restricted searching behavior[END_REF][START_REF] Barraquand | Animal Movements in Heterogeneous Landscapes: Identifying Profitable Places and Homogeneous Movement Bouts[END_REF]Cronin et al., 2013), or in the vertical dimension, using diving data (e.g. McConnell et al., 1992;[START_REF] Schreer | Classification of Weddell Seal Diving Behavior[END_REF][START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF]Fedak et al., 2001;[START_REF] Thums | A validated approach for supervised dive classification in diving vertebrates[END_REF][START_REF] Viviant | Can We Predict Foraging Success in a Marine Predator from Dive Patterns Only? Validation with Prey Capture Attempt Data[END_REF].

Diving marine top predators evolve in an environment where resource is considered to be aggregated, as it is considered to be patchily distributed at different spatial scales [START_REF] Fauchald | Foraging in a Hierarchical Patch System[END_REF]. Following the Optimal Foraging Theory, it is expected that predators' movements are characterized by an optimisation of food acquisition while costs to access this resource are minimized (Stephens and Krebs, 1986). In the horizontal dimension, at-sea foraging movements of central place foragers (Orians and Pearson, 1979), such as pinnipeds, are shaped by the need to often return to a haulout site. It is then expected that predators should focus their foraging activity in restricted areas where prey density should be high (Kareiva and Odell, 1987;[START_REF] Fauchald | Foraging in a Hierarchical Patch System[END_REF]Thums et al., 2011). This behaviour is called Area-Restricted Search (ARS) (Kareiva and Odell, 1987). In the vertical dimension, under-water movements of air-breathing diving predators such as seals are shaped by the physiological constraint of regularly returning to the surface to breathe [START_REF] Kooyman | Techniques used in measuring diving capacities of Weddell Seals[END_REF]. Following the optimal diving theory which is a special case of the optimal foraging theory, such predators tend to maximize the time spent at depths where they can find resources before being forced to go back to the surface because of the physiological constraint (i.e. oxygen needs) [START_REF] Kramer | The behavioral ecology of air breathing by aquatic animals[END_REF][START_REF] Houston | The optimal allocation of time during the diving cycle[END_REF]Carbone and Houston, 1996). These two theories provide a fundamental framework for the assessment of predators' foraging behaviour.

Consequently, studies on the foraging behaviour of diving marine predators have taken into account these two constraints in the horizontal and vertical dimensions to build their analyses on location and/or dive data (e.g. Thompson et al., 1991;Fedak et al., 2001;Fauchald and Tveraa, 2003;[START_REF] Mori | The Behavioral Basis for Nonlinear Functional Responses and Optimal Foraging in Antarctic Fur Seals[END_REF]Austin et al., 2006).

The first studies which aimed at assessing the foraging behaviour of diving marine predators generally based their analyses on horizontal or vertical movements separately (e.g. [START_REF] Bovet | Spatial analysis of animals' movements using a correlated random walk model[END_REF][START_REF] Thompson | Summer Foraging Activity and Movements of Radio-Tagged Common Seals (Phoca vitulina. L.) in the Moray Firth, Scotland[END_REF]McConnell et al., 1992;[START_REF] Schreer | Classification of Weddell Seal Diving Behavior[END_REF]McConnell et al., 1999;[START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF][START_REF] Baechler | Dive shapes reveal temporal changes in the foraging behaviour of different age and sex classes of harbour seals (Phoca vitulina)[END_REF]. Horizontal approaches were mainly based on the assumption that predators may change their movement pattern and increase their residency when encountering a patch of prey, thus implying a decrease of horizontal speed and a change of direction along its path (i.e. ARS behaviour) [START_REF] Johnson | Animal movements and population dynamics in heterogeneous landscapes[END_REF][START_REF] Fauchald | Scale-Dependent Predator-Prey Interactions: The Hierarchical Spatial Distribution of Seabirds and Prey[END_REF]. Accordingly it can be considered that animals spend most of their time in areas where they can probably benefit from a significant amount of food. Conversely, faster movements in straight lines are considered to be associated with transit between prey patches or with exploratory behaviour to find beneficial prey patches. The First Passage Time (FPT) is one of the most popular approaches allowing the assessment of ARS along an animal's track (Fauchald and Tveraa, 2003). In the vertical dimension, dive characteristics are used to infer the behaviour of diving marine predators. On the same principle as the horizontal dimension, it is acknowledged that during a foraging dive, predators should allocate most of their time at depths where they find food. Depending on the ecology and on the behaviour of a predator, foraging behaviour can be characterized by different dive patterns: pelagic predators are expected to perform 'wiggles' dives (i.e. sinuous dives) at a specific depth into the water column to purse pelagic prey, while benthic predators are expected to perform U-shaped dives on or close to the sea floor to catch benthic prey (Hindell et al., 1991;Thompson et al., 1991;[START_REF] Boeuf | Swim speed in a female northern elephant seal: metabolic and foraging implications[END_REF]Bjørge et al., 1995;[START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF]Beck et al., 2003;[START_REF] Bowen | Linking predator foraging behaviour and diet with variability in continental shelf ecosystems: Grey seals of eastern Canada[END_REF][START_REF] Bost | Changes in dive profiles as an indicator of feeding success in king and Adélie penguins[END_REF][START_REF] Heerah | A New Method to Quantify within Dive Foraging Behaviour in Marine Predators[END_REF].

Numerous studies characterized marine predators' foraging dives by using dive metrics obtained from telemetry devices, including dive duration, dive shape, ascent and descent speeds, bottom phase duration, presence or absence of wiggles at the bottom phase, inter-dive duration, or diving bouts (e.g. [START_REF] Schreer | Classification of Weddell Seal Diving Behavior[END_REF][START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF]Fedak et al., 2001;[START_REF] Baechler | Dive shapes reveal temporal changes in the foraging behaviour of different age and sex classes of harbour seals (Phoca vitulina)[END_REF]Austin et al., 2006;[START_REF] Robinson | A comparison of indirect measures of feeding behaviour based on ARGOS tracking data[END_REF]Ramasco et al., 2014).

In addition to inferring the predators' foraging behaviour from clues, some studies tried to measure predation events by using specific bio-logging devices such as video cameras (e.g. [START_REF] Bowen | Prey-dependent foraging tactics and prey profitability in a marine mammal[END_REF][START_REF] Watanabe | Dive depths of Weddell seals in relation to vertical prey distribution as estimated by image data[END_REF]; stomach-temperature sensors (e.g. [START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF][START_REF] Kuhn | Time to eat: measurements of feeding behaviour in a large marine predator, the northern elephant seal Mirounga angustirostris[END_REF] or jaw-or head-accelerometers (e.g. [START_REF] Suzuki | Validation of a device for accurate timing of feeding events in marine animals[END_REF][START_REF] Viviant | Prey capture attempts can be detected in Steller sea lions and other marine predators using accelerometers[END_REF][START_REF] Guinet | Southern elephant seal foraging success in relation to temperature and light conditions: insight into prey distribution[END_REF][START_REF] Viviant | Can We Predict Foraging Success in a Marine Predator from Dive Patterns Only? Validation with Prey Capture Attempt Data[END_REF]. These devices provided a way to accurately assess when and where prey capture attempts occurred.

They allowed to characterize at a small spatial and temporal scales which behaviour type is associated with foraging effort and/or foraging success, and consequently strengthened or weakened the previous hypotheses made on horizontal and vertical movements. For instance, in the horizontal dimension, Thums et al. (2011) were the firsts to provide the evidence that ARS behaviour are associated with a high foraging success for southern elephant seals. In the vertical dimension, [START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF] provided the evidence that most of the harbour seals' foraging events occurred during U-shaped dives.

Recent studies suggested that it may be more relevant to study simultaneously the horizontal and vertical movements of diving predators to determine their foraging effort with more accuracy, rather than separately (e.g. Bailleul et al., 2008;[START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF]Bestley et al., 2015). Accordingly, several studies tried to combine and/or to compare tracking data in the horizontal dimension and dive data in the vertical dimension in order to improve the assessment of diving predators' foraging behaviour, especially on phocid species [START_REF] Robinson | A comparison of indirect measures of feeding behaviour based on ARGOS tracking data[END_REF]Bailleul et al., 2008;[START_REF] Kuhn | Time to eat: measurements of feeding behaviour in a large marine predator, the northern elephant seal Mirounga angustirostris[END_REF]Thums et al., 2011;[START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF]Bestley et al., 2015;[START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF]. Most of these studies focused more particularly on southern and northern elephant seals, which are large phocid species performing long foraging trips at a large spatial scale (e.g. [START_REF] Kuhn | Time to eat: measurements of feeding behaviour in a large marine predator, the northern elephant seal Mirounga angustirostris[END_REF]Thums et al., 2011;[START_REF] Guinet | Southern elephant seal foraging success in relation to temperature and light conditions: insight into prey distribution[END_REF]. Some authors tried to compare results obtained solely from analysis of the horizontal movements, with results obtained from both horizontal and vertical movements' analyses, and they concluded that approaches that combine the two dimensions may be more appropriate to identify the foraging effort of diving predators (Bailleul et al., 2008;[START_REF] Bailleul | Spherical First Passage Time: A tool to investigate area-restricted search in three-dimensional movements[END_REF]Bestley et al., 2015).

Besides, the assumption was that the behaviour as observed from the surface may sometimes not be relevant for diving marine predators' foraging behaviour (Weimerskirch et al., 2007;Bailleul et al., 2008;Bestley et al., 2015). By contrast, other authors compared foraging clues used in the horizontal and vertical dimensions, and founded a good relationship between the two dimensions (Thums et al., 2011;[START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF][START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF]. Hence, all studies do not provide the same outcomes on what extent the horizontal and vertical dimensions can be representative of diving predators' foraging behaviour. This diversity of results according to different studiesprobably associated with the choice of the approach, the scale of the behaviour studied, the species studied -highlights the uncertainties on the possible relationship between horizontal and vertical dimensions in foraging effort assessment. This is especially the case for phocid species other than Southern and Northern elephant seals, that are lacking in the literature.

Here, we compared a horizontal vs. a vertical approach to identify the foraging areas of two sympatric phocid species, the harbour seal (Phoca vitulina) and the grey seal (Halichoerus grypus). Most previous studies on these two species focused on the ability of trip characteristics and diving data to predict their foraging behaviour, however they rarely focused on the ability of these indices to spatially characterize foraging areas. We consequently aimed at comparing areas identified by the two approaches, in the horizontal and vertical dimensions, in order to see if they provide consistent results.

Hence, we applied a horizontal approach at a trip scale (First Passage Time analysis to detect areas of restricted search, ARS) and a vertical approach at a dive scale (selection of foraging dives), which are both based on the theory that seals foraging behaviour is shaped by the need to optimize food acquisition (i.e. optimal foraging theory). We therefore expect to see if most of the dives associated with a foraging behaviour in the vertical dimension occurred where seals performed an ARS behaviour in the horizontal dimension.

Material and Methods

Telemetry data

Study area and study period

The study was conducted in the eastern English Channel which is characterized by shallow waters (maximum depth around 60-70 m) and strong tidal currents. Both harbour and grey seals forage in this area and haulout along the coasts of Northern France on tidal estuaries (Baie de Somme, Baie d'Authie, Baie de Canche) or on coastal sandbanks (Banc de Walde). Many of these grey seals also use a long offshore sandbank (GoodWin Sands) in the North-Eastern part of this area which is the Southern British haulout site for this species.

We focused on harbour seals' and grey seals' foraging behaviour in the Eastern part of the English Channel, as assessed from telemetry data obtained from individuals captured in the Baie de Somme (France) in 2008 and 2012. This estuary is the major French haulout site for the largest reproductive colony of harbour seals in mainland France and is also used by an increasing number of grey seals this last decade (Hassani et al., 2010;Vincent et al., 2017).

We removed data obtained during the breeding and moulting periods as it was already documented that seals' foraging behaviour is largely influenced by the need to be close to or at haulout sites during these two periods [START_REF] Fedak | The energetics of lactation: accurate measurements from a large wild mammal, the Grey seal (Halichoerus grypus)[END_REF]Boness, 1984;[START_REF] Anderson | Grey seal males: energetic and behavioural links between size and sexual success[END_REF]Thompson et al., 1994). Our aim was to focus on the tracking period when seals need to replenish their body reserves before the next breeding and moulting periods, enhancing their foraging activity (Thompson et al., 1994;McConnell et al., 1999;Beck et al., 2003;[START_REF] Breed | Sex-specific, seasonal foraging tactics of adult grey seals (Halichoerus grypus) revealed by statespace analysis[END_REF].

Assessment of foraging locations First Passage Time analysis (horizontal approach)

The First-Passage Time (FPT) analysis is a heuristic method which aims at measuring changes in the general pattern of an animal's movements along a pathway in order to detect Areas of Restricted Search behaviour (ARS) (Fauchald and Tveraa, 2003). For a moving animal, the ARS behaviour is characterized along a pathway by a decrease of the horizontal speed and by an increase of the turning angle, i.e. by an increase of residence time in this area (Kareiva and Odell, 1987). Considering that top predators such as seals live in a heterogeneous environment, it is expected that these predators should change their behaviour when they encounter patches of high prey density, and may consequently perform an ARS behaviour (Kareiva and Odell, 1987;[START_REF] Benhamou | How animals use their environment: a new look at kinesis[END_REF]. Because the FPT corresponds to the time required for a tracked animal to cross a circle of a given radius r, this analysis can provide a quantified information of time residency in areas at a selected scale r (Fauchald and Tveraa, 2003).

The analysis consists in calculating the FPT along a trip by sliding different circles of radius r to detect which one is the most representative of an ARS behaviour (Fauchald and Tveraa, 2003). The analysis was performed for each single trip performed by seals.

The FPT was calculated every 0.25km along each trip for circles of radius r varying from 0.5 to 5km by 0.5km, from 5 to 10km by 1km and from 10 to 100km by 10km. The analysis failed for some very short trips: no FPT values could be calculated for trips with less than 20 segments of 0.25 km. High FPT valued were expected in areas where the animal performed an ARS behaviour. However, because FPT is a scale-dependant measure (i.e. FPT increases with the tested scale r), it was recommended to logtransform FPT data and to focus on the variance of these log-transformed FPT values series merged by scales r (Fauchald and Tveraa, 2003). It was assumed that the maximum in var[log (FPT)] should be associated with the most representative scale r for an ARS (Fauchald and Tveraa, 2003). Consequently, the most representative scale r was determined for each trip when the analysis was possible, and the FPT dataset associated selected.

The [START_REF] Lavielle | Detection of multiple changes in a sequence of dependent variables[END_REF][START_REF] Lavielle | Using penalized contrasts for the change-point problem[END_REF] non-parametric segmentation process was applied to select parts of the trips associated with an ARS behaviour [START_REF] Barraquand | Animal Movements in Heterogeneous Landscapes: Identifying Profitable Places and Homogeneous Movement Bouts[END_REF], in order to divide each studied trips in 𝐾 segments, homogeneous in terms of mean and/or variance. We used the package adehabitatLT including the functions lavielle, chooseseg and findpath in R 3.4.1 (R Core team, 2017) in order to estimate the number 𝐾 of segments which made up the time series of FPT values, and then to identify the limits between different segments in time series. This segmentation process is based on a contrast function, that is to say the contrast measure between the real time series and the segmented time series, in term of mean and/or variance here. It takes into account this contrast function and tends to minimize it [START_REF] Barraquand | Animal Movements in Heterogeneous Landscapes: Identifying Profitable Places and Homogeneous Movement Bouts[END_REF]. The 𝐾 value was automatically selected by detecting the presence of a break in the decrease of contrast function when focusing on it according to 𝐾 value. Here the break is set when the limit of the second derivate of standardized contrast function is reached (according to previous findings the limit was set at 0.75; [START_REF] Lavielle | Using penalized contrasts for the change-point problem[END_REF]. Finally, each trip with at least one ARS was divided in 𝐾 segments, and we defined that at most half of the trip's segments detected could be associated with an ARS, indeed these segments correspond to those with the highest mean of FPT: when 𝐾 was even, we associated 𝐾/2 segments with an ARS behaviour, and when 𝐾 was uneven, 𝐾/2 -1 segments. We finally focused on GPS locations in ARS to assess seals' foraging areas.

The ability of FPT analysis to detect an ARS is highly influenced by the accuracy of individual trips, and more specifically by the number of locations data registered during a trip [START_REF] Pinaud | Quantifying search effort of moving animals at several spatial scales using first-passage time analysis: effect of the structure of environment and tracking systems[END_REF]. We performed a sensitive analysis to quantify this ability to detect an ARS by focusing on the influence of the number of GPS locations per trip on the probability to detect an ARS along these trips. Two logistic models, one for harbour seals and one for grey seals, were tested in order to describe these two parameters. These models were considered to be statistically significant when 𝑝 < 0.05. We finally quantified the proportion of time spent at sea associated with a probability of at least 0.95 to detect an ARS.

Dive criteria-based analysis (vertical approach)

The vertical approach developed in this study aims at applying two foraging criteria to select likely foraging dives. Harbour and grey seals are benthic foragers (e.g. Thompson et al., 1991;Bjørge et al., 1995), which was confirmed, at least for harbour seals, by local studies of their diet mainly composed of demersal or benthic preys (Spitz et al., 2015). According to the optimal diving theory, it is admitted that harbour and grey seals usually perform "U-shaped" dives close to the bottom to forage (Thompson et al., 1991;Bjørge et al., 1995;[START_REF] Schreer | Classification of Weddell Seal Diving Behavior[END_REF][START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF]Beck et al., 2003;[START_REF] Bowen | Linking predator foraging behaviour and diet with variability in continental shelf ecosystems: Grey seals of eastern Canada[END_REF]. This was confirmed for instance by [START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF] using stomach-temperature telemetry: 89% of harbour seals' feeding events occurred during U-shaped dives against 11% during V-shaped dives. The vertical approach is based on the benthic characteristic of seals' foraging behaviour and focuses on two behavioural variables: the dive shape and vertical descent speed. This approach was applied on dives with a maximum depth ≥ 3 meters and a dive duration ≥ 30 seconds, considering that very shallow and short dives unlikely match with a foraging behaviour as seals have a benefit to early terminate very shallow dives if they do not encounter prey quickly [START_REF] Thompson | How long should a dive last? A simple model of foraging decisions by breath-hold divers in a patchy environment[END_REF].

The dive shape was assessed with the Time Allocation at Depth (TAD) index (Fedak et al., 2001). It is a dimensionless index assessing the dive shape by considering the estimated Minimum Coast of Transport Speed (MCTS) in order to avoid the influence of descent and ascent phases on its calculation. It is independent from the dive duration and maximum depth, and informs on the two-dimensional time-depth dive profile, i.e.

where the animal focused its activity (equation 1). The TAD index usually varies from 0 when the seal spent most of its diving time close to the surface with only a brief descent to the maximum depth, to 1 when it spent most of its diving time at the maximum depth (U-shaped dive). A TAD value of 0.5 is obtained when the diver spent equal time at all dive depths, and the dive is therefore V-shaped. In some case where vertical speed is faster than the estimated MCTS and where the seal spent all the "available" diving time at the maximum dive depth, the TAD value then exceeds 1. The S value (estimated MCTS), which is a crucial parameter to assess dive shape from the TAD index, was determined for each individual tracked. Following Fedak et al. (2001), we calculated the TAD values for all S values ranging from 1.0 to 3.0 m.s -1 by 0.1 m.s -1 in order to assess the best S value. This determination was achieved by plotting number of TAD values between 0.5 and 1.0 as a function of S values tested, indeed S value was selected when the curve plot reached an asymptote (Vincent et al., 2016). We focused on higher TAD values to select U-shaped dives. In order to take into account the age and sex variability in individual seal foraging strategies (e.g. [START_REF] Baechler | Dive shapes reveal temporal changes in the foraging behaviour of different age and sex classes of harbour seals (Phoca vitulina)[END_REF]Austin et al., 2006), we assessed individual TAD criteria for the assessment of individual foraging dives. We choose these criteria on the boxplot distribution of individual dives according to TAD, indeed we set the minimum TAD threshold close to the 3 rd quartile (i.e. 75% of dives) of each boxplot with an increment of 0.05. This choice was made in order to select at least 25% of the more U-shaped dives performed by each seal.

Although U-shaped dives performed by harbour and grey seals are usually associated with a foraging behaviour, it is also known that seals can sleep on the seafloor in shallow areas, then also producing U-shape dives (Thompson et al., 1991). Indeed sleeping or resting dives are generally characterized by a low vertical activity, especially during descent and ascent phases, while foraging behaviour is rather associated with a high vertical activity (Thompson et al., 1991;[START_REF] Lesage | Functional classification of harbor seal (Phoca vitulina) dives using depth profiles, swimming velocity, and an index of foraging success[END_REF]Sjöberg and Ball, 2000;Ramasco et al., 2014). Here we calculated the vertical descent speed during the first 10% of total dive duration and used it to determine which U-shaped dives seemed to be related to resting rather than foraging. In this way, for each seal, we proposed to look after the plots of the vertical descent speed of dives according to their duration. Here we observed that long U-shaped dives with a very low vertical descent speed were located close to haulout sites. Because it was already admitted that resting at sea behaviour generally occur in the vicinity of seals' haulout sites (e.g. [START_REF] Thompson | Summer Foraging Activity and Movements of Radio-Tagged Common Seals (Phoca vitulina. L.) in the Moray Firth, Scotland[END_REF], we supported that low vertical descent speed was a good criteria to distinguish resting U-shaped dives from foraging ones. Consequently, we chose a vertical descent speed criteria for each individual, based on plot distribution, to exclude long duration dives with a very low vertical descent speed from the analysis.

Comparison of foraging areas assessed from horizontal and vertical approaches

The seals' foraging areas were assessed by focusing on the spatial density of GPS locations in ARS for the horizontal approach and on the spatial density of foraging dives for the vertical approach. The spatial distribution of foraging areas was quantified on a grid of 5x5 km cells for both species. The spatial distributions of foraging areas assessed from the horizontal and vertical approaches were compared thanks to the Index of Differences in Spatial Pattern (IDSP) ( [START_REF] Lee | Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data[END_REF]Cronin et al., 2012). This index aims at comparing the proportion of points (here GPS locations in ARS or foraging dives) by grid cells and provides a unique value ranging from 0 to 1, where 0 corresponds to a complete overlap between two spatial distribution and 1 corresponds to no overlap at all (equation 2).

Equation ( 2):

IDSP = ∑ |𝑝2-𝑝1| 2
With: p1 = Proportion of GPS locations in ARS in a grid cell p2 = Proportion of foraging dives in the same grid cell

Here we quantified this difference of spatial distribution between foraging areas assessed by the two approaches both at individual level and also at species level (all individuals pooled).

Results

Tracking characteristics

Harbour and grey seals tagged in this study were tracked during 119±48 days on average. During the study period, grey seals performed most of their trips (80.0% of all GPS locations) within the study area. Harbour seals performed all of their trips within the study area, the Eastern English Channel. In the horizontal dimension, we focussed our analyses on 1,991 trips -780 for grey seals and 1 211 for harbour seals -which fully occurred in the study area and during the study period (Figure 1). These trips lasted 18.3±51.2 hours for grey seals and 18.2±29.6 hours for harbour seals on average. They included 34,620 GPS locations, representing on average 31.7±95.6 GPS locations per grey seals' trip and 8.2±15.9 GPS locations per harbour seals' trip (Table 1). In the vertical dimension, a total of 588,666 dives were registered during the studied trips and transmitted by the tags (Table 1).

Foraging behaviour assessed from the horizontal approach

At least one ARS was detected by the FPT analysis for 290 of the 780 grey seals' trips (individual percentage of 36.0±13.6% on average) against 538 of the 1211 harbour seals' trips (45.7±9.0%). On each of these trips with ARS, the FPT analysis detected 1.4±0.1 and 1.3±0.1 ARS per trip for grey and harbour seals respectively. On average, the ARS were detected along seals' trips at a larger scale for grey seals than for harbour seals, as the radius associated were on average 2.7±5.6 km vs. 1.1±1.6 km respectively.

About 22.3±15.1% and 46.8±16.6% of the total time spent at sea by grey seals and harbour seals respectively were associated with trips characterized by an ARS of 0.5km radius (the smallest spatial scale r tested). The scale of ARS performed by grey seals differed in terms of size from one individual to another. Individuals G02, G06 or G08 performed wider ARS behaviour (radius r close or beyond 6 km in average) than seals G03, G04 or G05 (radius r below 2.5 km on average) (Table 2). Lower inter-individual variations of the ARS scale were observed in harbour seals with most individual averages of ARS radius lower or equal to 2.0 km. On average grey seals made longer trips with ARS than harbour seals (42.1±78.5 hours vs. 31.2±36.7 hours respectively)

and showed a higher variability, with seals G02, G06 or G08 performing much longer trips (>60 hours on average) than for seals G03, G04 or G05 (<35 hours on average) (Table 2). For harbour seals the average duration of trips with ARS was under 40 hours, with the exception of seal S02 performing longer trips (48.2 ± 76.2 hours).

The time spent in the ARS detected represents 43.5% of the total time spent at sea by grey seals (766.9 days), against 36.8% of the total time spent at sea by harbour seals (1257.8 days). Among the total time spent in ARS by grey seals, only 13.6% of this duration was characterized by an ARS of 0.5 km radius (i.e. the smallest spatial scale r tested). For harbour seals, this value represents 48.7% of the whole time spent in ARS.

Three different trips performed by seals are presented in Figure 2, as an illustration of the main behavioural patterns assessed from the horizontal approach (FPT analysis).

Grey seal G08's trip was characterized by an offshore behaviour where the individual moved away from the haulout sites for about 12 days and performed a wide ARS of 30 km radius. In contrast, G05's trip was characterized by a more coastal behaviour where the seal was closer from the haulout sites, moved away for about 3 days, and focussed its activity at a smaller scale than G08 (ARS of 9 km radius). According to the Lavielle's segmentation process, two period were associated with an ARS behaviour during this trip, a longer one and a shorter one. Finally, harbour seal S04's trip was very short and lasted less than one day. It was characterized by an ARS behaviour at the smallest scale tested (0.5 km) which took place very close to the haulout site (baie de Somme). In this last case, results of the FPT analysis indicated that the harbour seal S04 focused its activity in a very restricted area along its trip.

The probability to detect an ARS behaviour along a trip for both harbour and grey seals increased with the number of GPS locations registered during this trip (logistic regression for harbour seals: d.f.1,1209 = 727.1, P < 0.001; logistic regression for grey seals: d.f.1,778 = 528.4, P < 0.001). Trips at sea should include a minimum of 10 GPS locations for harbour seals and 29 for grey seals, in order to be able to detect an ARS with a 0.95 probability (Figure 3). Such trips were performed by harbour seals and grey seals during 52.1% and 74.3% of their total time at sea by harbour seals and grey seals respectively.

Foraging behaviour assessed from the vertical approach

Grey seals and harbour seals tagged performed 210,788 and 377,878 dives respectively during the tracking period. The Minimum Cost of Transport Speed (MCTS) S was individually calculated and the average values estimated were 1.8 m.s -1

(1.4-2.1 m.s -1 ) for grey seals and 1.6 m.s -1 (1.4-1.8 m.s -1 ) for harbour seals (Table 3).

Then, we assessed the TAD index and the vertical descent speed for each dive to perform the vertical approach, indeed individual thresholds based on these two metrics were assessed and applied to select likely foraging dives (Table 3). The more U-shaped dives performed were selected by applying TAD thresholds which range from 0.8 to 0.95, according to each individual behaviour. The vertical descent speed thresholds were here exclusively assessed at 0.1 or 0.15 m.s -1 depending on individuals (Table 3). The application of these two foraging criteria allowed us to select 59,297 foraging dives for grey seals (30.7±8.2%) and 88,987 for harbour seals (25.2±9.1%).

We decided in this study to present the foraging dives selection process for three representative seals: the grey seals G05 and G08 and the harbour seal S04. For these examples, the distribution of dives according to their shape (TAD values) highlighted different inter-individual strategies. Indeed, the grey seal G08 performed more U-shaped dives than the grey seal G05, and the harbour seal S04 performed a strategy closer to G08 than G05 (Figure 4). Firstly, the TAD thresholds were set at 0.8 and 0.9 respectively or grey seals G05 and G08 respectively, and at 0.9 for harbour seal S04. This first criteria permitted to select at least 25% of the more U-shaped dives for each individual (Figure 4). The determination of vertical descent speed threshold is here presented as an example for harbour seal S04. By plotting the vertical descent speed of each dive as a function of their duration, we were able to distinguish long duration dives with a very low descent speed from those with a upper descent speed (Figure 5). Here the threshold was set at 0.10 m.s -1 in order to exclude dives with very low values of vertical descent speed from the analysis.

Comparison of the two approaches

The foraging areas assessed by the horizontal and vertical approaches were spatially compared both at specie level and at individual levels. In the case of grey seals, the two maps permits to identify the highest densities of GPS locations in ARS (horizontal approach) and of foraging dives (vertical approach), considered to be representative of seals' foraging areas. Kernel density at 75% more especially permits to identify the main foraging hotspots as assessed by the two approaches. For grey seals, the main foraging areas assessed by horizontal and vertical approaches were the same, indeed the same hotspots were highlighted (Figure 6). The IDSP value -calculated to quantify the spatial difference between the foraging areas assessed -is 0.35 for grey seals. This is a low value and it confirms a strong overlap between the two spatial distributions at the specie level. The most visible difference is that foraging dives are more widespread than GPS locations in ARS. At the individual level the values of IDSP range from 0.23 to 0.50, and it highlights that the accordance between the two approaches is also dependant on individual results. For harbour seals, the highest densities of foraging events obtained from the horizontal and vertical approaches also strongly overlap, with one exception for a specific foraging area only assessed by the horizontal approach in the northern part of the grid (in the harbour of Boulogne-sur-Mer) (Figure 6). This difference is also highlighted by an IDSP value of 0.49, which is a higher value compared to grey seals. This means that the overlap of the foraging areas obtained by the two approaches is weaker for harbour seals than for grey seals, when pooling all individuals (i.e. specie level). However, at the individual level the values of IDSP range from 0.23 to 0.52, and are consequently close to IDSP values obtained for each grey seals, depending on individuals.

The likely foraging effort assessed from horizontal and vertical approaches was also quantified in term of duration, and more especially on the basis of total tracking duration and on the time spent at sea for each seal (Table 4). We estimated that grey seals spent 33.8% or 26.5% of their whole tracking duration foraging (from the horizontal vs. vertical approaches respectively), versus 26.9% or 21.5% for harbour seals respectively. We also looked at the time at sea spent foraging and estimated that grey seals spent 43.5% or 34.0% of their time at sea foraging (from horizontal vs. vertical approaches respectively), versus 36.8% or 29.4% for harbour seals respectively.

In general, the time spent foraging estimated for most of the individuals appears to be of a similar order of magnitude between horizontal and vertical approaches. It is also important to notice that the estimated time spent foraging is generally higher when assessed by the horizontal approach rather than by the vertical approach. However, the main sources of doubts generally concern some harbour seals for which it seems to have a significant difference between the estimated amount of time spent foraging when assessed by horizontal vs. vertical approaches. For example, it is particularly the case for harbour seal S02 for which there was a strong difference in the proportion of time spent foraging, indeed half of its time at sea was associated with foraging by horizontal approach while about only one-tenth by the vertical approach.

Discussion

This study is one of the first to propose a comparison of a horizontal vs. a vertical approach in order to focus on their ability to assess the seals' foraging areas. Two different approaches were compared as they are based on two types of behaviour at two different scales, in order to see if the foraging areas assessed separately did overlap. For the harbour and grey seals studied, the overlap between these areas was strong, consequently giving more confidence in the ability of these approaches to truly assess their foraging areas.

The key challenge of this study was to assess seals' foraging areas with no information on the ground truth, but only with location data in the horizontal dimension and summarized dive data in the vertical dimension (from GPS/GSM tags). Indeed, it was essential to use appropriate behavioural metrics for the specific data structure and for the species studied. Most of the previous studies on the use of horizontal and vertical movements to assess foraging behaviour concerned the Southern and Northern elephant seals (e.g. Bailleul et al., 2008;[START_REF] Kuhn | Time to eat: measurements of feeding behaviour in a large marine predator, the northern elephant seal Mirounga angustirostris[END_REF][START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF] which are known to forage at a very large scale. They were generally performed with ARGOS data in the horizontal dimension (e.g. McConnell et al., 1992;[START_REF] Robinson | A comparison of indirect measures of feeding behaviour based on ARGOS tracking data[END_REF]Thums et al., 2011) and high-frequency dive data in the vertical dimension (e.g. [START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF][START_REF] Bras | Three-dimensional space use during the bottom phase of southern elephant seal dives[END_REF], and sometimes with additional data on prey capture attempts (e.g. [START_REF] Kuhn | Time to eat: measurements of feeding behaviour in a large marine predator, the northern elephant seal Mirounga angustirostris[END_REF][START_REF] Jouma'a | Adjustment of diving behaviour with prey encounters and body condition in a deep diving predator: the Southern Elephant Seal[END_REF][START_REF] Vacquié-Garcia | Predicting prey capture rates of southern elephant seals from track and dive parameters[END_REF], this providing for now a good hindsight in the assessment of elephant seals' foraging behaviour. By contrast, for harbour and grey seals, which perform a more coastal behaviour at a smaller scale, there is still a lack of knowledges on how assessing their foraging areas with more accuracy. Consequently, by comparing foraging areas detected with a horizontal vs. a vertical approaches, we sought to provide prospects to better assess harbour and grey seal's foraging areas.

. The assessment of foraging behaviour at two different scales

The scale of behaviour studied is fundamentally different between the two approaches used here: the horizontal one is applied at a trip scale (tens of kilometres during several hours/days) while the vertical one is applied at a dive scale (several meters during several minutes). Indeed these approaches captured two different behaviours which can both characterize how predators spatially use their environment to forage. Horizontal movements illustrate the scale of the environment, while vertical movements rather represent the scale of the prey. It was previously shown that the incorporation of the vertical dimension in the analyses can provide a better estimate of the scale of foraging behaviour for diving predators such as seals (e.g. Bailleul et al., 2008). Conversely, approaches which incorporate movement analysis only in a horizontal dimension should be more appropriate for terrestrial predators, as they indicate where the animals spent most of their time in a two-dimensional plane.

However, to our knowledge, few studies on harbour and grey seals tried to compare the foraging areas assessed from the two approaches based on behaviour either in a horizontal or in a vertical dimension, and consequently to see if ARS behaviour in a two dimensional plane are associated with a high proportion of foraging dives.

Taking individual strategies into account

This study is one of the first to consider individual strategies performed by each seal to assess their foraging areas, by adapting individual thresholds to the methodology.

We assumed that these strategies should be taken into account to assess the foraging behaviour with more accuracy, since the possible differences in inter-individual strategies could be explained by the environmental variability of the targeted prey patches (i.e. types of prey, their density and distribution), by the physiology conditions of seals (e.g. dive capacities according to body size; [START_REF] Boyd | Dive durations in pinnipeds and seabirds[END_REF] and by the random aspect of behavioural choices (Beck et al., 2003;Austin et al., 2006;Sparling et al., 2007).

We chose to perform the horizontal approach (First Passage Time analysis) at the shortest scale possible (from 0.5 km of circle radius) in order to also capture searching behaviour (ARS) close to the haulout sites, by assuming that harbour and grey seals can also forage during the shortest trips. It is more particularly the case for harbour seals as they are known to forage close to haulout sites (e.g. Härkönen and Harding, 2001;Cunningham et al., 2009;Vincent et al., 2010;Vincent et al., 2017), while most studies on grey seals described foraging trips further away from their haulout sites (e.g. Sjöberg et al., 1995;McConnell et al., 1999;Sjöberg and Ball, 2000;Harvey et al., 2008). This difference is here highlighted by a significant amount of time at sea associated with horizontal movements at the smallest scale tested for harbour seals (46.8±16.6% of time at sea spent during trips with an ARS of 0.5 km radius, versus 22.3±15.1% for grey seals).

This result confirmed the importance to focus on movements at a small scale for this species. In the present study, grey seal movement patterns did vary, highlighting distinct individual foraging strategies. Some individuals such as G05 mostly performed an ARS behaviour at a small scale (ARS radius of 2.4±2.9 km during trips of 33.1±33.8 h), while others like G08 mostly performed an ARS behaviour at a larger scale (ARS radius of 6.0±11.6 km during trips of 76.4±156.4 h). To our knowledge, this is the first study on harbour seals' horizontal movements to use the First Passage Time in order to identify ARS behaviour, and to provide relevant results which can potentially be used to assess their foraging behaviour. It confirms the importance to consider small scale movements to accurately represent the searching behaviour of harbour seals, but also of grey seals if we assume that they can sometimes also forage close to haulout sites.

We developed a vertical approach based on the benthic characteristic of harbour and grey seals' foraging behaviour, taking into account the different individual dive strategies. Minimum Cost of Transport Speed (MCTS) was the prior metric individually calculated to strengthen the assessment of dive shape (TAD index) (Vincent et al., 2016), as it is linked to an energetic optimization which depends on metabolic capacities (e.g.

influence of body mass and buoyancy on transport costs; Williams, 1999; [START_REF] Beck | Seasonal changes in buoyancy and diving behaviour of adult grey seals[END_REF][START_REF] Williams | Sink or Swim: Strategies for Cost-Efficient Diving by Marine Mammals[END_REF]. Very few references of MCTS are reported in the literature for harbour and grey seals. Studies performed on captive seals in flume tanks estimated MCTS from 0.85 to 2.2 m.s -1 for harbour seals ( [START_REF] Davis | Swimming Metabolism of Yearling and Adult Harbor Seals Phoca vitulina[END_REF][START_REF] Williams | The evolution of cost efficient swimming in marine mammals: limits to energetic optimization[END_REF] and about 1.3 m.s-1 for grey seal [START_REF] Fedak | Circulatory responses of seals to periodic breathing: heart rate and breathing during exercise and diving in the laboratory and open sea[END_REF][START_REF] Thompson | How fast should I swim? Behavioural implications of diving physiology[END_REF][START_REF] Williams | The evolution of cost efficient swimming in marine mammals: limits to energetic optimization[END_REF].

The estimated values of MCTS in the present studies on wild seals (1.4-1.8 m.s -1 for harbour seals and 1.4-2.1 m.s -1 for grey seals), are higher than values from studies on captive seals, and it was already assumed that values obtained in captivity should be underestimated compared to wild animals [START_REF] Gallon | How fast does a seal swim? Variations in swimming behaviour under differing foraging conditions[END_REF]. We chose to estimate the MCTS for each seal rather than using a fixed value because this metric can critically influence the assessment of dive shape with TAD index if it is under-or over-estimated (Fedak et al., 2001).

The two individual criteria assessed in this study to detect foraging dives (i.e. vertical approach) are the threshold of minimum TAD allowing the selection of the more U-shaped dives, and the threshold of minimum vertical descent speed allowing the exclusion of resting or sleeping dives. Rather than setting the same thresholds values for all individuals, we have chosen to compare the distribution of these two metrics for each individual to determine appropriate thresholds. In this way, this allowed us to take into consideration inter-individual differences both behavioural and physiological.

Limits in the assessment of the foraging behaviour

The two methods used in this study imply limitations in the assessment of seals foraging effort and its spatial representation. One of them concerns the accuracy of the data used to perform the analysis of the seals' movements. In the horizontal dimension, the reliability of the results obtained through the analysis as the First Passage Time is strongly dependant on the accuracy of the tracking system, indeed a lower spatial resolution of the trips (horizontal movements) can directly affect the ability of FPT to detect ARS behaviour (Fauchald and Tveraa, 2003;[START_REF] Pinaud | Quantifying search effort of moving animals at several spatial scales using first-passage time analysis: effect of the structure of environment and tracking systems[END_REF].

Pinaud (2007) showed that the assessment of ARS is more challenging at very small scale due to the tracking system accuracy. In our study we used Fastloc GPS locations that give a higher accuracy as well as a higher frequency of acquisition, compared to study with other tracking systems as the ARGOS one (e.g. McConnell et al., 1992;[START_REF] Robinson | A comparison of indirect measures of feeding behaviour based on ARGOS tracking data[END_REF]Thums et al., 2011). Our decision to perform FPT analysis from a very small scale (0.5 km) is based on a compromise between taking into account the scale of the seals' behaviour (especially small for harbour seals) and the accuracy of the location data (high with the GPS tracking system). However we had to assume that the frequency of GPS locations acquisition was highly different between harbour seals and grey seals (7.9±5.9 vs. 31.1±17.5 GPS locations per day respectively). This result combined with a the smaller scale of movements performed by harbour seals than for grey seals (46.8±16.6% vs. 22.3±15.1% of the total time spent at sea during trips with at least one ARS of 0.5km radius) suggests that results concerning harbour seals might be less reliable than those on grey seals. This difference between the two species was also strengthened by the lower proportion of time at sea characterized by a probability of at least 0.95 to detect an ARS (52.1% vs. 74.3% for harbour and grey seals respectively).

We therefore emphasized that the lower reliability for harbour seals results than for grey seals ones may potentially due to tracking accuracy limitation (i.e. harbour seals might have performed ARS at a small scale non-detectable with the present data), as well as individual behaviour (i.e. harbour seals might have performed less ARS than grey seals).

In addition, it was already shown that the simple use of a horizontal approach could lead to simplistic interpretations of the diving predators' foraging behaviour, as their behaviour at sea is more complex (e.g. McClintock et al., 2013;Bestley et al., 2015;[START_REF] Carter | Navigating uncertain waters: a critical review of inferring foraging behaviour from location and dive data in pinnipeds[END_REF]. Indeed, the horizontal approach reduces the predators' movements into two behavioural states at sea: transit phase with a low residence time (supposed to be travelling) and ARS behaviour with a high residence time (supposed to be foraging).

Methods like FPT analysis has consequently the disadvantage to be deterministic and to not consider other types of behaviour such as resting at sea -at the surface or while diving -which was previously described in harbour and grey seals close to their haulout sites [START_REF] Thompson | Summer Foraging Activity and Movements of Radio-Tagged Common Seals (Phoca vitulina. L.) in the Moray Firth, Scotland[END_REF]Thompson et al., 1991). Horizontal movements can consequently be a source of confusions between foraging and resting behaviour since they are both characterized by a decrease of speed and a higher residency (e.g. Pinaud and Weimerskirch, 2007; Owen- Smith et al., 2010;Sommerfeld et al., 2013;[START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF]. We therefore assumed that a part of ARS of 0.5 km radius performed near haulout sites might not always match with foraging. It is also important to notice that predators such as seals are known to opportunistically forage during rectilinear movements (transit mode) (Thompson et al., 1991;Weimerskirch et al., 2007;Vincent et al., 2016), and the FPT misses this type of behaviour.

Compared to the vertical approach which is based on a dive scale, the horizontal approach can only provide the general pattern of searching behaviour at a trip scale.

However, despite these several limitations concerning horizontal approaches, we highlighted here that the simplified pattern provided by horizontal movementsassessment of areas of restricted search -provides consistent foraging areas when comparing with those assessed by a vertical approach.

Comparison of foraging areas assessed from the horizontal vs. the vertical approaches

We found a strong overlap between the foraging areas assessed from the two approaches for both grey seals (IDSP=0.35) and harbour seals (IDSP=0.49), when focusing at the specie level (all individuals pooled). The horizontal approach highlighted the densest foraging areas, and the vertical approach highlighted the same hotspots as well as more widespread, less densely used foraging areas. We therefore have confidence in stating that in this study the areas where seals restricted their search in a horizontal plane (ARS) were representative of areas characterized by the highest densities of foraging dives, as assessed in a vertical plane. This key result provides the demonstration that it is possible to assess the same main foraging areas with two different approaches even if they are based on two different behaviours at two different scales (ARS behaviour vs. benthic foraging behaviour). Consequently, the assessment of the seals' foraging areas was particularly strengthened by this comparative process.

These results are in agreement with other studies on other species, which have shown that the intensive foraging effort detected from the surface with location data (i.e. ARS behaviour) could be associated with proxies of foraging success estimated in the vertical dimension (Thums et al., 2011;[START_REF] Dragon | Horizontal and vertical movements as predictors of foraging success in a marine predator[END_REF][START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF]. [START_REF] Ramasco | The intensity of horizontal and vertical search in a diving forager: the harbour seal[END_REF] found that it was not possible to distinguish resting from foraging in the horizontal dimension. We therefore had to assume that foraging areas assessed with the FPT (horizontal approach) could potentially lead to an overestimation of real foraging effort. However, these studies and our results are in contradiction with other studies that showed that the relationship between the horizontal and vertical movements used to assess foraging can be weaker (Bailleul et al., 2008;Bestley et al., 2015). Indeed, they spotlighted that it can have a mismatch between the scale at which horizontal and vertical movements respond (environment scale vs. prey scale respectively), and consequently approaches which only focus on horizontal movements may misrepresent the scale of diving predators' foraging effort (Bailleul et al., 2008). We consequently made the assumption that this variability of relationships between the horizontal and vertical dimensions to assess foraging effort may be dependent on the species and/or the individual strategies, and probably on the scale at which the study is performed.

The comparative process of foraging areas was also performed at the individual scale and it provided the demonstration that taking individual strategies into account is critical for having more insight into the assessment of the seals' foraging areas. Indeed, while the accordance between foraging areas assessed by the two approaches was higher for grey seals than for harbour seals (IDSP of 0.35 and 0.49 respectively) at the colony scale (all individuals pulled), the difference was significantly lower at the individual level (average individual IDSP of 0.35 ±0.10 for grey seals vs. 0.35 ±0.12 for harbour seals). The individual variability of IDSP values within species highlighted that according to each seal, the relationship between their horizontal and vertical movements is more or less pronounced probably due to specific strategies. Indeed, even if we can emphasize with our results that most of the seals might have performed horizontal and vertical movements close to the optimal foraging theory and optimal diving theory respectively to forage (i.e. individuals with the lowest IDSP values), we cannot exclude that some individual should have not necessarily mostly performed ARS and/or U-shaped to forage. The harbour seal S02 could be an example of an individual which could have performed a foraging behaviour potentially different from the theories as the overlap between its foraging areas assessed by the two approaches is weaker compared to other seals (IDSP=0.52). These specific cases illustrates that even if proxies of foraging in horizontal and vertical dimensions are probably consistent to assess the main foraging areas of a seals colony, or of most of the individuals, it remains important to state that some specific cases linked to individual strategies could not be fully interpreted.

The amount of time spent foraging is rarely calculated or published, but we proposed to use it as an additional information for the assessment of foraging areas. It is important to note that in our study the estimation of time spent foraging at sea corresponds to the whole period associated with a foraging effort (time spent in ARS and time spent in foraging dives), rather than foraging success. We estimated that grey seals spent 43.5% or 34% of their at-sea time foraging (from the horizontal vs. vertical approaches respectively), versus 36.8% or 29.4% for harbour seals respectively. These estimations were close between the two approaches and of a consistent order of magnitude according to our knowledges on harbour and grey seals behaviour ( McClintock et al., 2013;Ramasco et al., 2014;[START_REF] Russell | Intrinsic and extrinsic drivers of activity budgets in sympatric grey and harbour seals[END_REF].

Conclusion

This study showed that most of the foraging areas assessed from the horizontal and vertical approaches were similar for harbour and grey seals in the eastern English

Channel. Consequently, we are confident in saying that the main foraging hotspots assessed in the two dimensions may be representative of the ground truth. However, none of the two approaches can be identified as providing the best estimate of the seals' foraging behaviour, as we had no data on actual prey captures. Our results suggest that comparing both approaches for the assessment of the foraging effort -and more especially foraging areas -in diving marine top predators gives more confidence in the obtained results.

Figures

Figure 1 -Maps of harbour and grey seals' satellite tracking used in this study and delimitation of the study area (filled grey), the Eastern English Channel. Lines in colours are associated with trips selected to perform the analysis, while lines in grey correspond to trips excluded. Figure 5 -Determination of the vertical descent speed threshold to exclude resting dives (second criterion of vertical approach). The dotted line indicates the threshold selected for the harbour seal S04 presented here as an example (0.1 m.s -1 ). This threshold excludes dives with a very low descent speed, considered to be associated with resting or sleeping. Tables Table 1 - A B S T R A C T Grey (Halichoerus grypus) and harbour seals (Phoca vitulina) are sympatric seal species, but they display distinct strategies of habitat use and connectivity between haulout sites. The distribution patterns and variations in relative abundance of both species were investigated along the French coast of the English Channel, at the southern limit of their range where seal numbers are increasing. Regular censuses conducted at all main haulout sites in mainland France showed significant seasonal variations at most sites, with more harbour seals counted during summer (breeding and moulting seasons), and more grey seals during summer only in the eastern English Channel. Trends in maximum haulout numbers at haulout sites showed a significant increase over the last five years, ranging from 9.7% to 30.9% per year for harbour seals, and from 5.8% (in the western English Channel) to 49.2% (in the eastern English Channel) per year for grey seals. These rates of increase in grey seal numbers are not linked to local pup production and most probably result from seal movements from the southwest British Isles and the North Sea, respectively. Aerial surveys conducted across the English Channel showed that most seal observations at sea were concentrated in the north-eastern English Channel. Telemetry showed that the 28 harbour seals tracked remained highly coastal, within a radius of 100 km from their haulout sites, and did not move to other known colonies. Grey seals moved much greater distances, reaching up to 1200 km from their capture site. More than half of the 45 grey seals tracked crossed the English Channel, especially during the breeding season, moving to known colonies in the southwest British Isles and the North Sea. Combining individual tracks and long-term surveys of the seal populations allowed a better understanding of the dynamics of these populations and their connectivity at a larger regional scale. The findings provide direct information for the management of grey and harbour seals within the frame of the Marine Strategy Framework Directive, and highlight focus areas where potential interactions between the two species should be monitored.

Introduction

Grey (Halichoerus grypus) and harbour seals (Phoca vitulina) are sympatric seal species with overlapping ranges in Europe and particularly around the British Isles (Jones et al., 2015). As central place foragers, their habitat preferences are constrained by the alternative use of the marine environment, particularly for foraging, and terrestrial haulout sites, for breeding, moulting and resting. During the breeding and moulting seasons, both species spend more time hauled out, while they spend comparatively more time at sea the rest of the year, in order to replenish their body reserves. In the northeast Atlantic, harbour seals breed in June-July and moult in August, while grey seals breed from September to December and moult between February and April. Harbour seals are slightly smaller than grey seals, and are usually reported to forage in shallower waters [START_REF] Bajzak | Habitat use by harbour seals (Phoca vitulina) in a seasonally ice-covered region, the western Hudson Bay[END_REF]. They also tend to move shorter distances from their haulout sites, and a number of studies have suggested that distinct haulout groups can be considered as discrete populations (Thompson et al., 1996;[START_REF] Huber | Genetic analysis of Washington State harbor seals (Phoca vitulina richardii) using microsatellites[END_REF][START_REF] Dietz | Movements and site fidelity of harbour seals (Phoca vitulina) in Kattegat, Denmark, with implications for the epidemiology of the phocine distemper virus[END_REF]. On the other hand, both juvenile and adult grey seals can exhibit long distance movements (Sjöberg et al., 1995;McConnell et al., 1999;[START_REF] Oksanen | Using movement data of Baltic grey seals to examine foraging-site fidelity: implications for seal-fishery conflict mitigation[END_REF], and a number of studies have shown that they can alternatively use different haulout sites during their annual cycle (Thompson et al., 1996;Russell et al., 2013;Jones et al., 2015). Despite these differences in distribution patterns, the two species overlap both at haulout sites and in their marine habitat use, and this raises questions about the ecological interactions between the two species [START_REF] Frungillo | An Analysis of Gray and Harbor Seal Strandings in Cape Cod, Massachusetts from 1999 to 2012[END_REF].

In recent years, the two species showed distinct trends in abundance, particularly in the core of their distribution range around the British Isles. Harbour seal numbers were increasing around the UK until the 2000s, then a number of colonies showed sharp declines in seal counts (up to -93% between 2000 and 2009) while others remained stable, and some continued increasing (SCOS, 2015). Differences in observed trends were attributed to high site philopatry in harbour seals, and therefore limited connectivity between different regions (Sharples et al., 2012). The most recent abundance estimate gives a total number of 36,500 (95% CI 29,900-49,700) harbour seals in the UK [START_REF] Scos | Scientific Advice on Matters Related to the Management of Seal Populations: 2014[END_REF]. This figure is close to the estimated 39,100 harbour seal abundance in the Wadden Sea in 2014 [START_REF] Pp | Report of the Working Group on Marine Mammal Ecology (WGMME)[END_REF]. In Ireland, the most recent estimate of 4153 harbour seals dates back from 2003 (Cronin et al., 2003).

Grey seal counts around UK have also shown contrasting trends according to regions in recent years, but they were either stable or increasing (Lonergan et al., 2011;[START_REF] Scos | Scientific Advice on Matters Related to the Management of Seal Populations: 2014[END_REF]. In 2013, a total population size of 111,600 (95% CI 92,000-137,900) grey seals were estimated around the UK [START_REF] Scos | Scientific Advice on Matters Related to the Management of Seal Populations: 2014[END_REF]. In Ireland, the grey seal breeding population estimate was 4409-7083 individuals in 2005 [START_REF] Cronin | Fishery-seal interactions in Irish waters: current perspectives and future research priorities[END_REF]. The species recolonized the Wadden Sea in the early 1990s after centuries of absence: a maximum of 3062 grey seals were counted during the moulting season in 2012, with a pup production of 412 pups in 2013/2014 (Brasseur et al., 2010).

Grey and harbour seals in France are at the southern limit of their range in the eastern Atlantic (Vincent et al., 2005;Härkönen et al., 2007a;Hassani et al. 2010). This paper focuses on the seals' distribution in mainland France, i.e. not including grey and harbour seals in Saint-Pierre & Miquelon (French overseas territory), in the western Atlantic. Until the early 2000s, there was a rather distinct distribution pattern between grey seals and harbour seals. The two principal grey seal haulout sites are located in Brittany, in the western part of the English Channel: the Molene archipelago (MOL) and the Sept Iles archipelago (SEP, Fig. 1), while the three harbour seal colonies are located in the middle and eastern parts of the English Channel: baie du Mont-Saint-Michel (BSM), baie des Veys (BDV), and baie de Somme (BDS, Fig. 1). More recently, grey seals have been increasingly observed at haulout sites in the eastern English Channel, including BDS, baie d'Authie (BDA) and Walde (WAL). Other haulout sites in Brittany and the eastern English Channel are used by increasing numbers of seals, although not exceeding tens of individuals, and therefore have not been included in this study (Fig. 1, 'minor sites'). During these last 15 years, a number of site-based telemetry studies were conducted in order to document the habitat use, movement patterns and activity rhythms of grey and harbour seals from several of these haulout sites along the French coasts (MOL, BSM, BDV and BDS; Vincent et al., 2005;Vincent et al., 2010;Huon et al., 2015). The results from these studies are all presented in this paper, in order to document the seals' habitat use and connectivity at a large scale.

We aimed to provide relevant information on the distribution and trends in abundance of grey and harbour seals in mainland France to local and national managers. Both seal species are protected at the national and European levels -all marine mammals are protected in France, while grey and harbour seals are listed in Appendix II of the Habitat Directive (1992/43/EC). 'Abundance and distribution of harbour and grey seals' (OSPAR Descriptor M-3) and 'grey seal pup production' (OSPAR Descriptor M-5) are also to be reported by European member states for the assessment of the Good Environmental Status (GES) within the Marine Strategy Framework Directive (MSFD, 2008/56/EC). The objectives of this study were to describe seasonal patterns and inter-annual trends in seal relative abundance at the main haulout sites, at-sea distributions of grey and harbour seals at the southern limit of their range in the Northeast Atlantic, and their connectivity with other colonies. By combining information on movements at sea and trends in seal numbers at haulout sites, we aim to better describe the population dynamics of both species in French waters, and their links with other colonies. Data presented here were obtained from regular censuses conducted by NGOs, Nature Reserves, MPAs and local authorities on seal haulout sites along the French coast of the English Channel, telemetry studies (including limited previously published data and new unpublished data on both species), and aerial surveys conducted on the marine megafauna in the English Channel in 2012 and 2014. Outcomes from this work will provide managers with updated information on MSFD indicators and ecological processes that may affect these indicators in coming years.

Material and methods

Censuses of seals at haulout sites

Census methods varied among study sites and species, and included visual observation from land, boat and aerial surveys over the haulout sites (Hassani et al., 2010;Vincent et al., 2005). In most cases, haulout sites are available to seals only at low tide. Timing of censuses (with respect to tidal conditions and time of the day) was adapted at each site to count seals during their peak haulout abundance. At all sites, censuses were scheduled at least once a month year-round, depending on weather. At some sites, especially in the northern part of the English Channel, censuses were more frequent, with up to one per day during the breeding season. During the breeding season, pups were counted separately. Given the low numbers of pups born (for both species at all sites), pup production was assumed to be accurately estimated from the number of pups counted during the censuses. For some study sites, censuses started in 1990, while for others they started only a few years ago. Counts of grey seals were classified into sex and age categories, however this was not possible for harbour seals. Numbers reported here therefore include all sex and age classes for each species. Only haulout sites with counts > 50 during at least one quarter of the year were included in these analyses.

Seasonal variations were assessed for each haulout site by fitting Generalized Linear Mixed-effects Models (GLMM) to the census data. Two models of seal counts were compared, one only including year of the census as a random effect and another one including both year of census and quarter of the year as random effects. We considered Quarter 1 (January-March, corresponding to the moulting season of grey seals), Quarter 2 (April-June, post-moult for grey seals and prereproduction for harbour seals), Quarter 3 (July-September, moulting and breeding seasons for harbour seals) and Quarter 4 (October-December, breeding season of grey seals). When more than one census per month was available, the maximum seal number for the month was selected. The two models were fitted in R and compared with a Poisson ANOVA ('lme4' package, R, version 3.2.4, 2016).

Long-term trends in seal abundance at haulout sites were reported from 1990 (for the earliest available) to 2015. A Generalized Linear Model (GLM) was fitted to the yearly maximum seal count at each haulout site, and the rate of increase was assessed from the outputs of the model.

Aerial surveys at sea

SAMM (Suivi Aérien de la Mégafaune Marine, Aerial Census of Marine Megafauna) aerial surveys were conducted during winter 2012, summer 2012 and winter 2014. Visual observations were recorded for the main taxa of marine megafauna, i.e. marine mammals, seabirds, sea turtles, large fish and elasmobranchs. Sampling design was a zigzag pattern to optimize searching effort and to cover the different marine habitats of the English Channel. Survey platforms were high-wing aircrafts, equipped with bubble windows; transects were flown at a target altitude of 180 m (600 feet) and a ground speed of 170 km/h (90 knots). Survey flights were only conducted during good weather conditions. For marine mammals, data were collected following a distance sampling protocol [START_REF] Buckland | Introduction to Distance Sampling: Estimating Abundance of Biological Populations[END_REF]. It is difficult to distinguish between harbour and grey seals when observed at sea. Consequently, these two species were grouped as 'seals' for data analysis.

A density map was drawn from the distribution of seal observations within a grid encompassing the whole English Channel area. Grid cells were 0.1°wide. Observations were weighted by the number of transects crossing each cell to take account of the difference in sampling effort. Only cells with sampling effort were indicated on the map.

Telemetry tracking of seals

A total of 45 grey seals (34 from MOL and 11 from BDS) and 28 harbour seals (6 from BSM, 12 from BDV and 10 from BDS) were tracked for more than a month between 1999 and 2014 (Table 1). In 1999In , 2002In and 2003, 15 , 15 grey seals fitted with Satellite Relay Data Loggers (SRDLs1 ), while from 2006 all other seals were fitted with Fastloc GPS/GSM tags2 (all provided by Sea Mammal Research Unit, UK). SRDLs transmit data via Argos [START_REF] Fedak | Overcoming the constraints of long range radio telemetry from animals: getting more useful data from smaller packages[END_REF] while GPS/ GSM tags record Fastloc GPS locations (Wildtrack Telemetry Systems) and transmit them with behavioural data through the mobile phone network ( McConnell et al., 2004). Seals were captured at their haulout sites with hoopnets, or in the water surrounding haulout sites using tangle nets. They were weighed to the nearest kilogram and anaesthetised with Zoletil (Virbac, France) at an intramuscular dose rate of approximately 0.8 mg kg -1 body weight (Baker et al., 1990).Tags were glued to the fur using quick-setting epoxy glue. Field work was conducted between April and July for grey seals (after their moulting period in March-April; Fig. 1S, Supplementary data) and in October for harbour seals (moulting in August; Fig. 2S, Supplementary data).

All locations were filtered following the methods of McConnell et al. (1992). For density maps, 20-min interval locations were interpolated from the real data to avoid over-estimation of coastal locations, since seals spend less time underwater at these locations and can therefore record a GPS location or send an Argos transmission more frequently. Density maps were drawn from the distribution of at-sea, interpolated locations within a 0.1°grid encompassing the whole English Channel area as well as the southern Celtic Sea. Locations were weighted by capture site (separately for grey and harbour seals) in order to take into account the number of days x seals of tracking available for each study site. This weighting did not involve the size of the haulouts however (estimated from the onshore counts).

Haulout data were obtained from the wet-dry sensor of the tags ( [START_REF] Photopoulou | The generalized data management and collection protocol for Conductivity-Temperature-Depth Satellite Relay Data Loggers[END_REF] and used to identify clear changes in activity rhythm of the seals during the breeding season. When a seal was located at a known breeding colony where it spent more than half of its time hauled out (i.e., no deep diving during several weeks), it was considered as engaged in reproduction. An example of such behavioural change is illustrated in Figs. 3S and4S (Supplementary data). On three occasions, visual observation confirmed that the seal was indeed breeding on the identified colony.

Results

Seasonal variations in abundance at haulout sites

Seasonal variations in seal numbers were significant for all harbour seal counts except those in BDV, while BDS and BDA showed significant seasonal variation (Table 2). Those seasonal variations in counts are illustrated in Figs. 2 and3 for grey and harbour seals respectively, by pooling counts from the last three years only (because of the global trends in seal numbers, see 3.2).

Although not statistically significant, seasonal variation in grey seal counts (from 2013 to 2015) were similar between the two western Channel study sites, which differed from all eastern Channel sites (Fig. 2). In MOL and SEP, counts at haulout sites peaked during the first quarter of the year, and showed their minimum during the breeding season. In BDS and BDA, seasonal differences in seal counts were significant with the highest counts during the third quarter (Fig. 2, p < 0.0001, Table 2).

In BSM, BDV, BDS and BDA, the maximum relative abundance of harbour seals appeared from July to September (Fig. 3). In BSM, BDS and BDA seasonal patterns in haulout counts were similar, with lower figures in the fourth and first quarters, intermediate in the second and maximum in the third quarter (Fig. 3). The largest harbour seal colony in mainland France is located in BDS, where over 470 seals were counted in the summer 2015 with a pup production of 87. The second most important colony is at BDV, with a maximum of nearly 200 harbour seals hauling out in the bay during the same period, and 40 pups born. Both the BDA and BSM had a peak of 80 harbour seals in the summer 2015, with 1 and 23 pups respectively.

Inter-annual trends in seal counts at haulout sites

Inter-annual trends in seal counts at haulout sites were examined using the seasonal maximum counts, i.e. censuses performed during quarter 3 for all harbour seal haulout sites, and for eastern Channel grey seal (BDS and BDA; censuses in WAL were too recent to be included), and during quarter 1 for MOL and SEP (cf. Section 3.1). Harbour seal numbers have increased at the four main colonies along the French coast since regular censuses began in the early 1990s (Fig. 4). The rates of increase are close to +10%/year in BSM, +15%/year in BDV and BDS, and exceed +30%/year in BDA (Table 2). Grey seal numbers increased exponentially at all studied haulout areas (Fig. 5). In Brittany (western English Channel) grey seal numbers increased by +5.8%/year and +8.3%/year (in MOL and SEP respectively, Table 2), but the rates of increase were much higher in the eastern English Channel with +21.4%, +49.2% and +49.0% per year in BDS, BDA and WAL respectively (Table 2). In addition to maximum seasonal counts (reported in Fig. 5), an exceptional count of 642 grey seals was made in Walde (WAL) in February 2014, when a strong storm passed over southeast England. This number is not reported here as it was counted outside the season of usual maximum relative abundance.

Distribution at sea

Density maps based on telemetry data highlighted the coastal movements of harbour seals, and the high usage of estuaries in proximity to haulout sites (Fig. 6). At BSM and BDV, most at-sea locations were obtained close to sandbanks with limited movements along the coasts, while harbour seals from BDS travelled along the coasts, away from the bay. Grey seals locations were also concentrated around haulout sites, both at MOL and BDS (Fig. 7). However, the density map highlighted high density of at-sea locations around Goodwin Sands. To a lesser extent, coastal areas located around the Isles of Scilly, Cornwall (England) or Bay of Seine (France) were also used, although the latter was not being used as a haulout. Almost all grid cells in the western and eastern English Channel were visited by at least one of the 45 grey seals included in the study, while about half of the cells located in central Channel were not visited.

During aerial surveys of the English Channel, observations of seals at the sea surface were generally scarce. A total of 61 seals were observed during the 24,608 km of effort conducted under good observation conditions. Cumulative encounter rates were similar between summer and winter with a total of 0.16 and 0.20 sightings per 100 km respectively. In the western part of the English Channel, only few observations occurred around MOL and off BSM in the vicinity of known haulout sites (Fig. 8). By contrast, a higher density of seals was recorded in the southern part of the North Sea where seals, probably mostly grey seals, were regularly observed swimming or resting at the sea surface in the Strait of Dover (particularly around Goodwin Sands) between the French and English coasts.

Individual movements and connectivity

Tag deployments provided 149 ± 55 days (n=45) of tracking per grey seal and 127 ± 47 days (n=28) of tracking per harbour seal (Figs. 1S and 2S,Supplementary data), excluding the six seals that were tracked for less than a month. Harbour seal tracks terminated prior to the breeding season, except for one adult male in BSM (Fig. 2S). Most grey seal tracks however covered the end of the breeding season or beginning of the moulting season, from October to January (Fig. 1S).

Harbour seal telemetry data revealed no connectivity between colonies (Fig. 9). In BSM, seals were tracked for 95 ± 45 days (n=6) and they all remained in the tidal range of the bay. In BDV, the average track duration was higher (138 ± 40 days; n=12) than at BSM, seals remained in the vicinity of their capture site, moving either north of the Bay (especially towards the St Marcouf islands) or along the coast, up to 30-70 km west or east (Fig. 9). One seal did cross the English Channel and reached the English coast, but its tracking duration was shorter than a month and was not included in this study. Harbour seals from BDS were tracked for 134 ± 53 days (n=10) and also showed coastal movements, up to a hundred km from the Bay south-or northwardly. Trips at sea did not exceed 15-20 km from the shore (Fig. 9).

Almost all the harbour seals in this study hauled out exclusively in the bays where they were captured, only one captured in BDS used another haulout site during the tracking period, located in BDA (15 km from BDS).

Grey seals moved over much greater distances than harbour seals 1 for the number of counts per site and species). Quarter 1=January to March; Quarter 2=April to June; Quarter 3=July to September; Quarter 4=October to December.

(Fig. 10). The duration of grey seal tracking was 146 ± 56 (n=34) and 161 ± 50 (n=12) days on average from MOL and BDS, respectively. Tags deployed in western Brittany (MOL) showed regular movements to colonies in the southwest British Isles, especially the Isles of Scilly (200 km from MOL), Cornwall and western Ireland (500 to 800 km from their capture site). Some seals also moved to Wales (n=2), and to eastern Scotland (n=1; 1200 km from MOL). Seals hauled out at established grey seal colonies along these coasts. Fewer trips occurred eastwards in the English Channel, only four seals hauled out or passed close to SEP, two other seals spent some time around the Channel Islands. Only one animal tagged in Brittany visited the eastern English Channel, a young female that made a long exploratory trip to the Thames estuary (Fig. 10). Among the 34 grey seals tagged at MOL, 35% remained within 50 km of MOL for the whole tracking duration, while the remainder crossed the English Channel and/or the Celtic Sea, with and4S). Only one adult female tracked during the breeding period gave birth to a pup in SEP (confirmed by visual observation). Grey seals tagged at BDS (n=11) also showed long-range movements. In addition to using alternative haulout sites along the French coasts, seven of twelve seals regularly visited Goodwin Sands (Fig. 10). Other haulout sites included those along the coasts of England (Norfolk) and Scotland (Scottish Borders), up to 750 km from BDS, and along the coast of the Dutch Wadden Sea, 600 km from BDS. Many of these colonies (in southern Scotland, Norfolk and the islands of the Wadden Sea) were visited during the breeding season (Fig. 10). Only two seals (tracked for 1 for the number of counts per site and species). Quarter 1=January to March; Quarter 2=April to June; Quarter 3=July to September; Quarter 4=October to December. 111 and 157 days) did not show movement away from the French coast.

Discussion

4.1. Seasonal and inter-annual trends in counts at haulout sites Over the last three years, harbour seal counts at haulout sites along the French coast were usually highest during the third quarter of the year, i.e. the breeding and moulting season. This was expected, as a higher proportion of seals haul out during these periods (e.g. [START_REF] Cunningham | Variation in harbour seal counts obtained using aerial surveys[END_REF]. In other areas however the maximum number of harbour seals hauled out has been reported later in the year (e.g. mid October after the moult, [START_REF] Cronin | Using mobile phone telemetry to investigate the haul-out behaviour of harbour seals Phoca vitulina vitulina[END_REF].

In this study, long term trends in harbour seal haulout counts showed a consistent increase. Trends were very similar at BDS and BDV over the last 20 years, with rates of increase close to +15% per year. At BSM the rate of increase is lower (close to +10% per year currently) while it was much higher at BDA (+31% per year). Geographically, BDA is much closer to BDS than BDV, and some seals tracked from BDS hauled out at BDA. The very high rate of increase at BDA might therefore not result from local production but rather from frequent movements between BDA and BDS, with an increasing use of this haulout site potentially reflecting an expansion of the nearby haulout group of BDS.

Grey seal haulout counts also increased over the study period. At MOL and SEP, the rate of increase reached +6%/year and +8%/year respectively. Haulout counts were highly variable however, and the maximum number of seals was not always counted during the moulting season. Difficult weather conditions during winter precluded a number of censuses over the years, so that data were unavailable for the first quarter in some years at these two sites. A maximum abundance at haulout sites during the moulting season is expected in grey seals, and has also been observed at the Isles of Scilly ( [START_REF] Leeney | Abundance, distribution and haul-out behavior of grey seals (Halichoerus grypus) in Cornwall and the Isles of Scilly, UK[END_REF]. Lower abundance during the breeding season is less expected though, as adult seals spend a larger proportion of time hauled out during reproduction than the rest of the year. At MOL, low counts at haulout sites are in accordance with observed movements to British or Irish grey seal colonies during the breeding season, as well as the very low pup production (potentially a few pups a year). At SEP, counts were the lowest during the fourth quarter, while 32 pups were counted at this site during the breeding season 2014. This suggests that, as in MOL, a significant proportion of the seals counted at this haulout site during the year breed elsewhere. In the eastern English Channel, grey seals have not reproduced successfully at any of the known haulout sites (although a few dead newborn pups were found in February in recent years) and the maximum number of seals hauled out was recorded during the third quarter, i.e. outside the breeding and moulting seasons. This maximum abundance during summer has already been reported at other sites in the North Sea (i.e. Thompson et al. 1996) and supports the hypothesis that these seals belong to a larger population, using a number of haulout sites during their annual cycle. The rates of increase of grey seals at BDS and BDA are much higher than in Brittany, with +21%/year at BDS and +49%/year at BDA and WAL. WAL is the closest site to the nearby Goodwin Sands (Kent, England), where many grey seals tracked from BDS hauled out. One striking example of this is the observation of 642 grey seals at WAL in February 2014, while a storm was hitting the coasts of Kent: it is suggested that a large proportion of the seals counted during the aerial surveys might not necessarily use the haulout sites censused along the French coast, but might instead use haulout sites located along the nearby English coast. Overall, given the observed connectivity with sites in the North Sea, increased haulout numbers of grey seals in the eastern English Channel are in accordance with the increasing grey seal numbers (and pup production) along the English and Dutch coasts of the North Sea (Brasseur et al., 2014;[START_REF] Scos | Scientific Advice on Matters Related to the Management of Seal Populations: 2014[END_REF].

At at least two sites (BDS and BDA), both species are now regularly observed together, whereas only harbour seals were present 10 to 15 years ago. The differences in rates of increase, the larger size of the grey seals and their wider distribution pattern, raise the question of potential inter-specific interaction. In the eastern English Channel as in other parts of their range, the two species' ranges overlap (Thompson et al., 1996;Jones et al., 2015). Ecological interactions between the two species have been documented by a number of studies (Bowen et al., 2003;[START_REF] Skeate | Likely effects of construction of Scroby Sands offshore wind farm on a mixed population of harbour Phoca vitulina and grey Halichoerus grypus seals[END_REF][START_REF] Frungillo | An Analysis of Gray and Harbor Seal Strandings in Cape Cod, Massachusetts from 1999 to 2012[END_REF]. Harbour seals are also known to be more susceptible to high mortalities during Phocine Distemper Virus (PDV) outbreaks (Hall et al., 2006;Härkönen et al., 2007b). Svensson (2012) estimated that under moderate or strong interspecies competition, rates of increase in the Baltic grey seal population would be a direct function of the prevalence of PDV, as it would reduce the competitive strength of harbour seals. Monitoring the abundance of these two species is recommended to evaluate the degree of interspecies competition.

Distribution and connectivity

Harbour seals remained very coastal during the study period, staying within 20 km from the shoreline at the three study areas (BSM, BDV and BDS). Along the coast, their movements did not exceed 100 km from the haulout site where they were caught. Such coastal movements have been described in the eastern ( McClintock et al., 2013;[START_REF] Blanchet | Harbour seal Phoca vitulina movement patterns in the high-Arctic archipelago of Svalbard, Norway[END_REF]Jones et al., 2015) and western Atlantic [START_REF] Bajzak | Habitat use by harbour seals (Phoca vitulina) in a seasonally ice-covered region, the western Hudson Bay[END_REF]. Distances covered by the seals from their haulout sites in this study (a few tens to a hundred kilometres) are similar to those reported elsewhere (Thompson et al., 1996;Härkönen et al., 2001;Cunningham et al., 2009;Bailey et al., 2014). However harbour seals, and especially juveniles, are known to cover greater distances in some areas, reaching several hundred or even thousand kilometres (e.g. [START_REF] Lowry | Movements of satellite-tagged subadult and adult harbor seals in Prince William Sound, Alaska[END_REF][START_REF] Björge | Dispersal and bycatch mortality in gray, Halichoerus grypus, and harbor, Phoca vitulina, seals tagged at the Norwegian coast[END_REF][START_REF] Dietz | Movements and site fidelity of harbour seals (Phoca vitulina) in Kattegat, Denmark, with implications for the epidemiology of the phocine distemper virus[END_REF][START_REF] Womble | Post-breeding season migrations of a top predator, the harbor seal (Phoca vitulina richardii), from a Marine Protected Area in Alaska[END_REF]. In this study the at-sea distribution was based on sub-adult and adult individuals.

Compared with harbour seals, grey seals showed a much wider distribution in the English Channel. Movements observed in the western English Channel were mostly transitional between MOL and other haulout sites located in the Southwest British Isles. In contrast, the majority of movements observed in the eastern English Channel represented central place foraging behaviour with long return-trips at haulout sites in BDS or Goodwin Sands. Grey seals around Ireland or the UK use offshore areas more so than harbour seals, with corridors between alternative haulout sites [START_REF] Cronin | Fishery-seal interactions in Irish waters: current perspectives and future research priorities[END_REF]Jones et al., 2015). Offshore movements of grey seals have also been described in other parts of the species' range (Harvey et al., 2008;[START_REF] Austin | Intraspecific variation in movement patterns: modelling individual behaviour in a large marine predator[END_REF]Breed et al., 2006). Although our results showed a lower density of seal locations in the middle part of the English Channel, no telemetry was conducted from the Sept Iles archipelago (SEP) nor from the Channel islands, where grey seals are known to haul out (Vincent et al., 2005). This gap in distribution may simply reflect the absence of tracking effort in this area. Because of the heavily biased ratio of males in our sample, we could not explore gender-based differences in distribution patterns, although such differences are known to occur in this species, potentially reducing intra-specific competition (Breed et al., 2006). Most adult females are known to leave MOL during the breeding season (Gerondeau et al., 2007), which is consistent with the very low pup production. While the number of tracked adult females from MOL was too low in our study to identify their breeding sites, one grey seal female photographed at MOL during summers 1998 to 2000 was previously photo-identified while breeding in the Blasket islands, western Ireland, in mid November 1996 (O'Cadhla and Hiby, pers. comm.).

Aerial surveys conducted in the English Channel in 2012 and 2014 did not detect a large number of seals in the middle or western part of the English Channel, but highlighted higher seal densities in the eastern English Channel/southern North Sea. Seals at sea can be cryptic because they spend the majority of their time diving, however aerial surveys provide a unique method for monitoring the at-sea distribution of the population [START_REF] Herr | Seals at sea: modelling seal distribution in the German bight based on aerial survey data[END_REF], while telemetry only allows shortterm tracking of a limited number of individuals. The higher number of seals observed in the eastern English Channel could be due to the proximity of the largest harbour seal colony in BDS, but it is rather suggested, given the high densities recorded further north and/or further offshore, that most of these seal observations were grey seals. This also concurs with the large size of the seals reported by the observers.

Connectivity between haulout sites was very different for the two species. No movement between the three harbour seal haulouts in France was observed, and only one movement to the UK suggested by an incomplete track of a seal captured in BSM (not shown). Movements between distinct harbour seal haulout sites have been reported in the Kattegat ( [START_REF] Dietz | Movements and site fidelity of harbour seals (Phoca vitulina) in Kattegat, Denmark, with implications for the epidemiology of the phocine distemper virus[END_REF], the western British Isles and the North Sea, including between the Wash (eastern England) and BDS (Sharples et al., 2012). Local site fidelity is considered high however, and connectivity between harbour seal haulout sites low (Sharples et al., 2012).

The present study showed connectivity between grey seal haulout sites within the study area and outside, both during and out of the breeding season. More than half of the tracked grey seals moved across the English Channel. In the western Channel, connectivity was high between MOL and the Isles of Scilly/Cornwall as well as western Ireland, and to a lesser extent with Wales and southwest Scotland. In the eastern English Channel, the main connections were between BDS and southeast England and the Wadden Sea, and less frequently to southeast Scotland and the Dutch coast. All of the haulout sites where tracked seals were assumed to be engaged in reproduction are known breeding grey seal colonies (e.g. Isles of Scilly and Cornwall, [START_REF] Leeney | Abundance, distribution and haul-out behavior of grey seals (Halichoerus grypus) in Cornwall and the Isles of Scilly, UK[END_REF]Horsey, Blakeney Point and Fast Castle, SCOS, 2013;Blasket Islands and Inishark;[START_REF] Kiely | Grey seal (Halichoerus grypus) pup production at the Inishkea island group, co. Mayo, and Blasket islands, co. Kerry[END_REF][START_REF] Cronin | Fishery-seal interactions in Irish waters: current perspectives and future research priorities[END_REF].

Strong breeding site fidelity in adult grey seals would explain genetic differentiation between colonies, despite extensive movements of individuals outside the breeding season [START_REF] Allen | Microsatellite variation in grey seals (Halichoerus grypus) shows evidence of genetic differentiation between two British breeding colonies[END_REF]. Although philopatry would be the general rule for female and male grey seals ( [START_REF] Pomeroy | Dispersion and site fidelity of breeding female grey seals (Halichoerus grypus) on North Rona, Scotland[END_REF][START_REF] Twiss | Dispersion and site fidelity of breeding male grey seals (Halichoerus grypus) on North Rona, Scotland[END_REF][START_REF] Pomeroy | Philopatry, site fidelity and local kin associations within grey seal breeding colonies[END_REF], immigration from colonies in the UK outside the breeding season was suggested to explain the rapid increase in grey seal numbers in Germany ( [START_REF] Abt | The dynamics of grey seals (Halichoerus grypus) off Amrum in the south-eastern North Sea -evidence of an open population[END_REF]. Density-dependent effects may lead to the establishment of new breeding colonies, as observed in the North Sea [START_REF] Gaggiotti | Patterns of colonization in a metapopulation of grey seals[END_REF]. Long range movements of grey seals shown here both during and outside the breeding season are therefore consistent with what was already known in this species in the core area of its range [START_REF] Hammond | Grey seals off the east coast of Britain: distribution and movements at sea[END_REF]McConnell et al., 1999). Russell et al. (2013) showed that between 21% and 58% of breeding females around the British Isles used different regions for breeding and foraging. In our study such long distance movements between distinct haulout sites were not observed in all tracked grey seals, with twelve of thirty-four individuals tracked from MOL remaining within 50 km of their capture site during the whole tracking period, often including the breeding season. This highlights the large variability in individual movement patterns [START_REF] Thompson | How fast should I swim? Behavioural implications of diving physiology[END_REF] as well as the high degree of site fidelity, at least seasonally [START_REF] Abt | The dynamics of grey seals (Halichoerus grypus) off Amrum in the south-eastern North Sea -evidence of an open population[END_REF][START_REF] Karlsson | Photoidentification, site fidelity, and movement of female gray seals (Halichoerus grypus) between haul-outs in the Baltic sea[END_REF]Oksanen et al., 2012).

Implications for management units

The monitoring of distribution and abundance of grey and harbour seals by EU member states is now expected under the Marine Strategy Framework Directive (MSFD). Two seal indicators were set up by OSPAR, indicator M-3 ('Abundance and distribution of harbour and grey seals') and M-5 ('grey seal pup production'), to coordinate reporting at a large regional scale, deemed more suitable than national levels for mobile species, such as grey seals. It was suggested that grey seals around the British Isles should be considered as a single population [START_REF] Hammond | Grey seals off the east coast of Britain: distribution and movements at sea[END_REF], hence indicator M-3 is calculated for a unique management unit for this species in Europe [START_REF] Ices | Report of the Working Group on Marine Mammal Ecology (WGMME)[END_REF]. Connectivity levels as well as frequencies of individual movements of grey seals tracked from the French haulout sites fully support the suggestion that seals in these areas belong to a larger population, centered around the British Isles. There seems however to be two very distinctive patterns of connectivity for the grey seal between the western and eastern part of the English Channel. The tagged animals showed frequent movements towards the southwest British Isles from MOL and the North Sea from BDS, whereas almost no exchange existed along the Channel itself suggesting that grey seals at the two extremities of the Channel belonged to distinct entities. Consistently, they also displayed very different seasonal patterns of abundance and long term trends in relative abundance. Grey seals observed at MOL clearly belong to a larger entity occupying the Irish and Celtic Seas, potentially reaching western Scotland, while grey seals of the eastern Channel belong to another entity widely ranging in the North Sea. As no such clear separation seems to exist in the northern British Isles [START_REF] Hammond | Grey seals off the east coast of Britain: distribution and movements at sea[END_REF], this suggests that grey seal distribution around the British Isles follows a horse-shoe pattern, with extremities at the western and eastern English Channel, separated by a few hundred kilometres (Fig. 11). This schematic view could be refined further when more data are available from the Irish Sea and the Channel Islands. However it illustrates the contrasting population dynamics and movement patterns observed from both ends of the Channel from the French coasts that suggest very limited connectivity. This situation is probably a consequence of the southern recolonization of the Northeast Atlantic grey seal population during the second half of the twentieth century (Duguy, 1980, this study), following two avenues along the western seaboard of the British Isles and through the North Sea, respectively. Genetic studies could confirm this hypothesis and quantify the differentiation of breeding colonies at both ends of this horse-shoe distribution [START_REF] Gaggiotti | Patterns of colonization in a metapopulation of grey seals[END_REF][START_REF] Wood | The genetics of recolonization: an analysis of the stock structure of grey seals (Halichoerus grypus) in the Northwest Atlantic[END_REF]. In terms of management units, while keeping one management unit for grey seals in the Northeast Atlantic (outside the Baltic) makes sense at the international level, the current situation of the species in France would justify managing haulout sites from both sides of the Channel as distinct units. It is expected that numbers of grey seals in the eastern Channel would continue to increase rapidly and that the distribution of the species at sea would gradually expand throughout the Channel, given the continuous increase in grey seal numbers reported in the southern North Sea (Brasseur et al., 2014;[START_REF] Scos | Scientific Advice on Matters Related to the Management of Seal Populations: 2014[END_REF][START_REF] Pp | Report of the Working Group on Marine Mammal Ecology (WGMME)[END_REF].

Management units of harbour seals in Europe are much smaller than for the grey seal, based on the more limited movement patterns shown by the species (ICES, 2014). One unit covers all haulout sites located along the French coasts of the English Channel. Most of these haulout sites seem to show similar trends in relative abundance, although some local disparity does exist. Genetic studies could be conducted in order to assess the degree of differentiation between the breeding colonies. [START_REF] Goodman | Patterns of extensive genetic differentiation and variation among european harbor seals (Phoca vitulina vitulina) revealed using microsatellite DNA polymorphisms[END_REF] identified six different units in European harbour seals, including one in eastern England, and showed that genetic differentiation increased with geographic distance. [START_REF] Huber | Genetic analysis of Washington State harbor seals (Phoca vitulina richardii) using microsatellites[END_REF] also showed that breeding harbour seal populations could be genetically distinct even when separated by distances similar to those between haulout sites along the French coast.

For both grey and harbour seals, most haulouts were located in Nature Reserves or Special Areas of Conservation (SACs), either in France or in the UK, Ireland and the Netherlands (Table 3). Virtually all harbour seal haulout locations were located in such protected areas (100% in BSM and BDS, 98% in BDV), all along the French coast. Grey seals tracked from MOL hauled out more in SACs than those tracked from BDS (93% and 66% respectively), and haulouts recorded in SACs outside France accounted for 16% and 21% respectively (Table 3).The time spent at sea (percentage of interpolated at-sea locations obtained from telemetry) lying within SACs and other Marine Protected Areas (such as marine National Parks and Offshore SACs) ranged from 51% to 77% for grey seals (BDS and MOL respectively) and 74% to 99% for harbour seals (BDS and BSM respectively, Table 3).These results highlight the relevance of the Natura 2000/MPA networks for the conservation of mobile species, and should encourage local and national conservation strategies from France, Ireland, the UK and the Netherlands to develop an international approach to the management of the grey seals in those protected areas. To our knowledge, this is the first time that these percentages of time spent in MPAs are estimated. While these figures could be expected for the comparatively more sedentary harbour seals, they highlight the relevance of such MPAs for both haulout and marine habitat use for the more mobile grey seals.

Introduction

The plasticity of behaviour consists of an array of behavioural responses to varying environmental conditions (Komers, 1997). It is predicted that the range of behavioural responses will increase with environmental variability. Hence, the wider the range of environmental change, the higher is the degree of behavioural plasticity. Animals adjust their behaviour with the trade-off between some costs and benefits. It appears that learning is a superior tool for fine-turning an animal to respond to environmental variations. It could be argued that behavioural plasticity based on learning is likely to be more continuous than a range of innate response. Furthermore, there are numerous correlates of the variation in social systems including sex ratio, predation pressure, and competition (linked to the population density and resources availability).

This behavioural plasticity can have an impact on animal habitat selection. The term 'selection' is defined as "the process by which an animal actually chooses a resource or habitat" (Johnson, 1980). Because different habitats are not equally available, habitat selection will occur when the habitat use is greater than would be expected, proportional to its availability. When the availability is evaluated, the accessibility has to be taken into account (Matthiopoulos, 2003), specifically in the case of central place foragers who need to periodically return to their terrestrial haulout sites or colonies.

Moreover, central place foragers exploit resources within a range of a 'central place' (e.g.

breeding sites for seabirds, haulout sites for pinnipeds). Because resources look by the animal are mainly linked to the prey availability, it appears important to previously focus on foraging activities before studying foraging habitat selection. The animal behaviour can specifically change from a specific area to another one due to the difference environmental conditions (habitat availability) and prey availability between the different areas; indeed the animal can adapt his behaviour to the local resource and habitat availability. Because we aimed at studying foraging habitat selection in a specific area, we thought important to focus on the behaviour carried out within this area, rather than focussing on general animal behaviour, indeed the study becoming "centred habitat". Hence, our study has the particularity to spotlight the importance of taking into account animal local behaviour.

Recent advances in biotelemetry and statistical analyses techniques have facilitated the study of spatial use to predict habitat selection of top marine predators (Andersen et al., 2013;Bailey et al., 2014;Wakefield et al., 2011). Generally habitat selection studies performed on central place foragers tracked at sea include all at-sea activities (travelling, resting, foraging), (Aarts et al., 2008;Huon et al., 2015). However, as resource availability influences habitat selection, it is important to focus primarily on foraging activities when considering habitat selection.

As central place forager, grey seals use haulout sites to breed, moult, rest and lend themselves well to telemetry studies as they can be captured and tagged at these terrestrial sites. In this study we used telemetry data from grey seals tagged on the eastern and western seaboards of Ireland to examine the difference of behavioural plasticity of two different colonies of grey seal using trip characteristics and foraging habitat selection models in relation to habitat availability. Grey seals are generalist animals that are individually specialized according to the habitat availability, variability and quantity of available preys (Gosch et al., 2014). They are also considered to be opportunistic generalist predators, consuming a wide variety of demersal and small fish ( Austin et al., 2006). In this study, we tagged grey seals at two different colonies in Ireland, at Raven Point (County of Wexford, eastern seaboard) and at the Great Blasket (Irish continental seaboard). Some grey seals tagged at the Blasket Island moved from the continental shelf area to the Hebrides in Scotland. The resource and habitat availabilities are not the same between eastern seaboard, the continental shelf and the Hebrides area. The continental shelf is an open area subject to different currents and thermal front, where occur an upwelling (Raine and McMahon, 1998), carrying a high production of phytoplankton and, consequently of upper trophic levels. On the opposite, the Irish Sea is a "closed" area with shallower depths. The seabed habitats and the species composition of different communities may particularly differ e.g. between easterly and west southerly facing coasts, resulting mainly from different temperature regimes [START_REF] Boelens | Ireland's Marine and Coastal Areas and Adjacent Seas: an environmental assessment[END_REF]. The Hebrides Malin Sea is also subject to different thermal front, "closing" this area and allowing also a high production of phytoplankton but with different species compare to the continental shelf [START_REF] Gowen | Plankton distributions in relation to physical oceanographic features on the southern Malin Shelf[END_REF].Due to the grey seals biology, their behavioural plasticity and depending of the environmental availability, the seal behaviour should not be the same between the continental shelf of Ireland and the northwest coast of Scotland (the Hebrides). As we tried to focus on the "local behaviour" around the Irish continental shelf, we turned our analyses to habitat centred to this area for the western part of Ireland. Indeed, we selected trips performed from these two colonies in order to focus on this centred habitat around the continental shelf of Ireland, located between the two main grey seal colonies of West Ireland (i.e. the Blasket Island and Belmullet). On the opposite, we did not perform trip selection for the eastern part of Ireland because we tried to centre our study on foraging habitat selection around the Irish Sea, as all tracks were included within this area. This trip selection around the continental shelf allows having two more suitable sizes for habitat selection comparison with the eastern seaboard, depending of the behavioural plasticity. We used trip characteristics and foraging habitat selection to examine the behavioural plasticity of these two colonies in Ireland, subject to different environmental pressure.

Material and methods

Ø Study Area

Study sites on the Atlantic continental shelf of Ireland (western seaboard) and the Irish Sea (eastern seaboard) were selected to reflect broad habitat differences (Figure 1). The most recent census provided a national breeding population estimate of 7,284 -9,365 grey seals in Ireland (O Cadhla et al 2013). The key colonies on the southeast coast of Ireland are located at the Raven Point (52°20'41''N-6°20'51"W, Wexford) and the Saltee Islands (52°07'30''N -6°35'50''W). Seals were captured at the Raven Point haul-out site where between 100 and 800 individuals come ashore to moult, breed and rest (numbers at the haul-out site vary seasonally, Cronin et al., 2013). The second study area on the western seaboard of Ireland reflects continental shelf habitat, extending up to 385 km from the coast at its widest point. Seals were captured at the Great Blasket Island (An Blascaod Mor, 52°05'45''N -10°32''27'W) in the Blasket Island Group in southwest Ireland. This colony is one of the largest breeding and moulting grey seal colonies in Ireland, where up to 1200 individuals come ashore to moult annually between Jan-April ( Cronin et al., 2013).

Ø Seal handling and tagging

Sixteen grey seals were tagged in February /March 2009-2012 at the Blasket Islands site and 19 seals were tagged at Raven Point site, in March/April 2013 and 2014 to coincide with the completion of the annual moult (Cronin et al., 2012). Seals were tagged with Fastloc TM GPS/GSM tags produced by the Sea Mammal Research Unit (SMRU) at the University of Saint Andrews, Scotland (Mcconnell et al., 2004). Seals were captured ashore in individual hoopnets. They were weighed to the nearest Kg and anaesthised intravenously using 0.5mg/Kg of Zoletil (Virbac, Carros France), (Baker et al., 1990). The tag uses a wet/dry sensor to determine whether the seal was hauled out, swimming at the surface, or diving. A haulout event started when the sensor was continuously dry for more than 10 min and ended when the tag was continuously wet for 40 seconds. GPS locations were filtered to eliminate those that led to unrealistic swim speeds (>2m/s; McConnell et al., 1992). To reduce sampling bias (between areas where the seal spent more time diving or out of the water), all GPS locations were interpolated every 20 minutes using a straight-line interpolation. A dive event started when the tag crossed a chosen depth threshold 1.5m and ended when the tag returned to a depth shallower than 1.5m. Dive events were summarised to starting and ending time (date and hour), maximum depth, recovery duration, diving transitional depths. The information is summarized every 1/10 th of dive duration to minimise data volume for transmission.

Location and dive data were recorded and stored on board the tag and relayed ashore when the seal came to the surface within range of the coastal GSM mobile phone network.

Ø Telemetry data included in the analyses

Analysis focussed on associations between foraging behaviour and habitat, using foraging dives. During the grey seal breeding period (September-December in Ireland, Cronin et al., 2013), sexually mature seals spend less time foraging and behaviour during this period is not typical of the wider population. Tracks from this period were therefore excluded from further analyses for animals that changed their behaviour (i.e. the ratio between haulout and dives).

Concerning the Irish Sea, we included all tracks, and only tracks performed on the Irish continental shelf for the other part.

Ø At-sea trip characteristics

To examine the degree of plasticity between and within sites, and to see if the inter-site plasticity is higher than the intra-site one, we compared 'trip characteristics' of seals tagged at both sites. We examined the trip duration and the maximum extent from the haulout per trip (both were log transformed to correct for non-normal distribution).

Because they are regular in time and space, we used interpolated tracks to determine these two parameters. We supposed that each trip within an individual was independent from the others. Each trip characteristic was compared between individuals within site and between the two sites using linear mixed effect models statistical analyses with R version 3.0.

Ø Foraging habitat selection

We applied a dive-based criteria analysis to identify grey seals' foraging behaviour ( Planque et al. in prep). It was performed on dives with a maximum depth ≥ 3 meters and a dive duration ≥ 30 seconds, considering that very shallow and short dives unlikely match with a foraging behaviour. This method is based on the assumption that grey seals are benthic foragers (Bjørge et al., 1995;Thompson et al., 1991) and that their vertical movements are close to an optimum when they search for prey (Optimal Diving Theory, (Carbone and Houston, 1996), which is based on Optimal Foraging Theory, (Stephens and Krebs, 1986) We therefore applied two diving criteria indicating the seals' benthic foraging behaviour: the dive shape and vertical descent speed (Vincent et al., 2016). These criteria were assessed individually because of possible inter-individual dive strategies linked with physiological or behavioural characteristics (Austin et al., 2006;Beck et al., 2003). Dive shape was assessed through TAD index (Fedak et al., 2001) which mainly vary between 0 and 1 where 0 correspond to dives close to surface and 1 to dives close to a "U-shape", i.e. dives where seals may likely forage (Bjørge et al., 1995;Hindell et al., 1991;[START_REF] Leboeuf | Continuous, deep diving in female northern elephant seals, Mirounga angustirostris[END_REF]Thompson et al., 1991). We applied individual TAD thresholds in order to select the more U-shaped dives performed by each seal, i.e. dives with a TAD value close to 1. Indeed, their individual determination aimed at selecting likely proportion of dives from a seal to another. The vertical descent speed criterion was also individually determined. Indeed, a threshold was chosen in order to exclude long duration U-shaped dives with a very low vertical descent speed from the analyses because they more probably match with low activity dives, i.e. resting while diving (e.g.

( Ramasco et al., 2014;Thompson et al., 1991) The process of the dive-based criteria analysis used to asses likely foraging dives, including thresholds determination, is described by Planque et al. (in prep).

To assess the foraging habitat selection of the tagged seals we compared environmental characteristics of used points (i.e. foraging dives locations) to those of random points that represent available habitat (Aarts et al., 2008;Johnson et al., 2006;Keating and Cherry, 2004;Lele and Keim, 2006). The random points were created locally within the study area using the package sp within R (R. Coreteam 2016). Typically, the number of random points varies between 1 and 5 times the number of telemetry locations [START_REF] Pearce | Modelling distribution and abundance with presence-only data[END_REF]Wakefield et al., 2011). Two random points were created per dive point. One buffer was created for each area (Figure 3). The size of each buffer was 158,428 Km 2 and 80,168 Km 2 for the continental shelf and the Irish Sea respectively.

The size of the buffer was determined as three times the size of grey seal home range for each area and depending also on the habitat data availability (avoiding the continental slope beyond 500m, outside the dive range of grey seals); see paragraph below. The random points associated with dive points were created within the buffer for each study area.

We used three environmental variables in the habitat selection model: bathymetry, tidal current and sediment (Figure 4) based on previous studies of seal habitat selection (Aarts et al., 2008;Bailey et al., 2014;Huon et al., 2015). The bathymetry dataset was obtained from the European Marine Observation and Data Network (EMODnet)

including bathymetric data from survey datasets (corresponding to GEBCO database) and composite Digital Terrain Model (DTM). GEBCO dataset corresponds to the 30" gridded data; and the DTM has a spatial resolution of 1/8 of arc minutes. Sediment data were obtained from the Mapping European Seabed Habitat (MESH) project and included outputs of the MESH-EUNI model, with a spatial resolution of 300m, which predicts habitat types as polygons. Sediment types were based on a simplified FOLK classification system (Folk, 1954) and limited to the most dominant types: rock, mud, sand, gravel, coarse (gravel and mud), and mixed (gravel and sand) sediments. Tidal current data were provided by the Irish Marine Institute. These data correspond to the outputs of a numerical model providing surface tidal current at 3 hourly intervals with a spatial resolution varying between 1.2 Km and 1.5 Km. From these data, we used a 6 months tidal current mean, corresponding to the seasonal tracking duration (April to September). This allowed the quantification of the tidal current strength in space irrespective of instant tidal phases (ebb, slack, or rising tide).

We calculated the geodesic distance from each dive and random point in space to the last haulout visited to describe accessibility to the environment ( Aarts et al., 2008). This distance was calculated using the LC.dist function in the marmap package (R) (Pante and Simon-Bouhet, 2013). Distance from shore was calculated as the straight-line distance to the closest point along the coast using ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA) 'Nearest' function.

We fitted Generalized Additive Mixed Models (GAMM) to the data, with the gam function, (package mgcv) in R (R Core team 2016) using a binomial family argument with a logit link function to estimate the parameters of an inverse-logit selection model based on seal foraging locations and random points (Johnson et al., 2006), for the two study areas.

Dive locations and random points were the response variable, taking the value 1 and 0 respectively. To take into account intra-individual autocorrelation, we treated individual as a random effect and environmental variables as fixed effects. The best model was selected using the AIC criteria (Akaike's Information Criteria, Akaike, 1973).

Furthermore, we calculated the importance of each covariate with the prediction function of the "GAMM", providing an index of the relative importance of each covariate in the chosen model. The maps of habitat use predicted by the model were created in ArcGIS for both sites.

Results

Ø Tracking data

A total of 19 grey seals were captured and tagged at Raven Point between 2013 and 2014, of which 8 provided sufficient data and were included in the analyses (Table 1).

Individual tracking durations ranged from 36 to 278 days (122 ± 84 on average), transmitting a total of 33,099 GPS locations. We calculated 55,398 interpolated locations, from which 41,701 were at-sea locations, used for the analyses. Tags transmitted 179,605 dives, 73,504 (40.92%) of which were identified as foraging dives and were included in the model.

A total of 16 seals were captured and tagged on Great Blasket Island between 2009 and 2012, of which 10 provided sufficient data and were included in the analyses (Table 1).

Individual tracking durations ranged from 74 to 287 days (191±77 on average), transmitting a total of 34,144 GPS locations. We used these GPS locations to calculate 97,582 interpolated locations.

The distance performed from the tagging site according to the tracking duration was plot for each seal (see supplemental data). For seals tagged at the Great Blasket, 4

travelled outside the study area (on the continental shelf) to the Hebrides in Scotland.

They crossed a mean distance of 575 Km (± 125 Km) from their tagging site. On the opposite, grey seals tagged at Raven Point crossed a mean distance of 135 Km (± 125Km) from their tagging site. These four grey seals tagged at the Great Blasket Island crossed long distances from their tagging site compare to those tagged at Raven point.

That is why we exclude trip performed outside de continental shelf area.

Eighty five percent of interpolated points were trips performed on the continental shelf,

for seal tagged at the Great Blasket Island. From this track selection, 334,620 dive locations were obtained, 119,938 (36%) of which were identified as foraging dives, which were retained for habitat selection analyses.

Ø Trip characteristics

Only trips with duration higher than 3h were used (trips with duration lower than 3h

were considered to be linked to the haulout activities). In each case, we applied Kruskal-Wallis test because the normality and homoscedasticity were not respected. As there were some long trip outliers from the Blasket Islands, we compare median values of trip metrics between study locations. In the case of inter-site plasticity comparison, the median trip duration of grey seals were significantly higher for the Irish Sea (23.91 ± 48.48hr) than for the continental shelf (8.92 ± 35.68hr), (Kruskal-Wallis pvalue =2.83*10 -13 , Figure 5). The median maximum extent travelled per trip was also significantly higher in the Irish Sea (6.24km ± 26.64km) compared to the continental shelf (1.87km ± 40.92km), (Kruskal-Wallis pvalue = 6.73*10 -8 , Figure 5). Intra-site plasticity differences were also observed for the Irish Sea (Kruskal-Wallis pvalue=7.64*10 -14 ) and for the Continental Shelf (Kruskal-Wallis pvalue= 6.66*10 -9 ), (Figure X). Dunn test was used as post hoc test to do an individual pair comparison. We observed some significant differences between some individual pairs (pvalue<0.05, Table X). Intra and inter-site plasticity were observed (Figures X and X), however the inter-site plasticity was higher than the intra-site one.

Ø Foraging areas

Foraging areas identified from dive characteristics are presented in Figure 6, showing kernel densities of selected foraging dive locations. For the continental shelf, high foraging effort was highlighted around the Blasket Islands, west of Galway Bay and north of the InishKea Islands. For the Irish Sea, foraging effort was concentrated around the southeast (Wexford and the Saltee Islands), along the coast of Dublin, southwest of the Isle of Man, and along the north coast of Wales.

Ø Foraging habitat selection

The optimal model for habitat selection of grey seals tagged at the Blasket Islands retained all covariates. The deviance explained by this model was 67%. Distance from the last haulout site and bathymetry account for most of the explained deviance in the model (57% and 27% respectively) while the distance from shore, tidal current and sediment types accounted for less than 20% (Fig. 7). The optimal model for foraging habitat selection of seals tagged at Raven Point in the Irish Sea also retained all the covariates. The deviance explained by the model was 66%.

The relative contribution of each covariate to the model was as follow: distance from the last haulout site (72%), bathymetry (10%); the distance from shore, the tidal current and sediments accounted all for less that 20% (Fig. 7). Grey seals tagged at Raven Point selected foraging habitat close to their haulout sites, decreasing with bathymetry up to 90m depth. Tidal current had a positive influence on foraging habitat selection, with grey seals favouring tidal current speeds of 0.6 m/s. The distance from shore had a weak influence on foraging habitat selection with only a very slow increase observed out to a distance of 70 Km from the coast. In contrast to the western seals, grey seals in the Irish Sea selected muddy or sandy habitats and avoided coarse, mixed sediment and rock. The highly selected foraging habitat was located around the Saltee Islands, and Wexford to the coast around Dublin (Fig. 8). Other patches of preferred foraging habitat were observed between northern Wales and the Isle of Man and between Cornwall and Wales. We used two trip characteristics to explore the potential effect of intra and inter site behavioural plasticity on spatial usage (linked to intrinsic factors, population structure, resource availability, density dependence) in our two grey seal colonies: the maximum extent per trip and the trip duration. Intra and inter site plasticity were observed.

Discussion

However, the inter-site plasticity was higher than the intra-site plasticity. It is quite normal to find a degree of intra-site plasticity, as grey seal is a generalist species composed by specialist individuals. However, grey seals tagged at Raven Point made trips of significantly longer in extent and duration. This cannot be due to the effect of density dependence, as the size of colony at the Blasket Island is the same at the Raven Point. Population structure could play a role, as the body size and the age of the individual influences foraging strategy. Indeed Cronin et al. (2013) found that larger female grey seals travelled further than smaller females from the Blasket Islands. Body size potentially influences foraging strategy through its effects on fasting ability and seasonal energy storage (e.g. [START_REF] Brodie | Cetacean Energetics, an Overview of Intraspecific Size Variation[END_REF]. It is likely that both environmental and endogenous factors shape behavioural plasticity in pinnipeds, and foraging patterns will vary geographically according to resource availability and physical features of the area.

The waters off the west and southwest coasts of Ireland are recognised as one of the most biologically productive regions in the eastern North Atlantic (ICES 2003) and it is likely that there is a much higher prey available to grey seals on the continental shelf than in the Irish Sea, minimizing intraspecific competition at the Blasket Island colony.

Furthermore, interaction between grey seals and human activities such as fisheries can occur. High level of interactions between fisheries and grey seals in the Irish and Celtic

Seas was recently highlighted (Cronin et al., 2012), whilst low overlap and low resource competition between grey seals and fisheries was found on the Irish continental shelf (Cronin et al., 2012;[START_REF] Houle | Effects of seal predation on a modelled marine fish community and consequences for a commercial fishery[END_REF]. Factors including prey availability and potentially higher interspecific competition with fisheries may explain this difference of behaviour between the Irish Sea and the continental shelf.

The second part of this study was to evaluate the influence of environmental variables on potential differences in foraging habitat selection between the continental shelf and the Irish Sea. Compared to previous studies which aimed at assessing grey seals' habitat selection by using all locations data without preselection, we proposed to specifically focused on foraging habitat selection by performing our models on foraging dives.

Compared to previous studies on grey seals' habitat selection (Aarts et al., 2008;Huon et al., 2015) we specifically focused on foraging activities, including just foraging dive locations in the model instead of all dive locations. The deviances explained for the model selected for each site were relatively high: 67% and 66% respectively for the continental shelf and for the Irish Sea. The variable explaining most of the model deviance was the distance from last haulout site (57% and 72% respectively for the continental shelf and the Irish Sea) followed by bathymetry (respectively 27% and 10%

for the continental shelf and the Irish Sea), this was the case for both sites. This can be expected, as grey seals are central place foragers and demersal feeders. The other environmental variables accounted for less than 20% of the deviance. These variables (i.e. sediment, distance from shore and tidal current) are specifically linked to habitat and prey availability and will vary geographically, explaining the differences observed in the relative importance of these variables between the Irish Sea and the continental shelf.

The distance from haulout was the most important factor influencing the foraging habitat selection of grey seals in both study areas. The habitat selected decreased rapidly from the last haulout site to a distance of 280 Km in the Irish Sea (east coast) and 250km on the continental shelf (west coast). Grey seals tagged in Ireland conducted at least 87% of their trips within 50Km around their haulout sites. As central place foragers, grey seals need to come back to terrestrial sites to rest, breed and moult, and our data shows that they spend the majority of their time close to haulout sites. This is consistent with other studies throughout the range of grey seals in the northeast Atlantic. Grey seals in the North Sea spent 43% of the time within 10km of the haulout sites ( McConnell et al., 1999) and preferentially selected habitat closer to haul-out sites with a gradual decrease of habitat selection beyond tens of kilometres (Aarts et al., 2008). In the Baltic Sea, (Sjöberg and Ball, 2000) noted short distance trips (from 10 to 15 Km), spending 75% of their time within a radius smaller than 50Km around their haulout site. In the Iroise Sea (France), grey seals mostly undertook trips within 40km of haulout sites (Huon et al., 2015).

As grey seals select foraging habitat close to their haulout sites, the bathymetry should have a negative influence on their foraging habitat selection (decreasing more or less gradually from the shore or islands where haulout sites are located). Foraging habitat selection by grey seals in the Irish Sea and on the continental shelf whilst primarily influenced by distance to haulout site, was also influenced to a lesser extent by bathymetry. For seals tagged at Raven Point, the bathymetry had first a negative influence; the foraging habitat selection decreased until depth of 75m and reaches a plateau. For grey seals tagged at the Blasket Islands, the bathymetry also had a negative influence out to the 100m contours beyond which it increased. This bimodal response of grey seals to bathymetry was also observed in the Iroise Sea off France. In general grey seals selected shallow waters close to their haulout sites, but some seals selected deeper waters in specific areas (i.e. canyon), (Huon et al., 2015). (Jessopp et al., 2013) demonstrated that grey seals off Irelands west coast generally made benthic dives, with the majority of dives in water depths greater than 50m, noting a maximum dive depth of 455m. Our findings (that seals selected foraging habitat on the western seaboard where water depth was greater than 100m) are in accordance with this earlier study. Grey seals are benthic feeders (Thompson et al., 1991) and the influence of bathymetry on foraging habitat selection will presumably vary locally depending on the seabed topography and sediment type.

Along the continental shelf, grey seals favoured rocky habitat and avoided sandy and muddy foraging areas, while in Irish Sea they mainly selected sandy, muddy and rocky foraging habitats. They avoided habitats with coarse and mixed sediments. Sediment type can be used as a proxy of prey distribution, as sediment type influences the distribution and abundance of available prey. Jessopp et al. (2013) found that grey seals tagged at the Blasket Islands foraged over a range of habitats, from fine sediments as mud/sand through to 3 dimensionally complex rocks. The diet of grey seals in the area consists primarily of demersal gadoids and sandeel (Gosch et al., 2014) which concurs with seal habitat use observed by Jessopp et al. (2013), demersal gadoids being generally associated with rocky and gravelly habitat and sandeels with coarse and medium sand [START_REF] Holland | Identifying sandeel Ammodytes marinus sediment habitat preferences in the marine environment[END_REF]. Our findings of low selection of sand/mud habitat do not necessarily disagree with the earlier study by Jessopp et al., (2013) which noted foraging over multiple habitats, but did not assess habitat selection, while our own results do not preclude foraging over these habitats, merely a selection for rocky habitats. Given that the study area for the continental shelf is dominated by sandy substrate (which represented 82% of the dives and random points), some foraging over this substrate is expected regardless of strong selection of other substrates.

Furthermore, as generalist feeders, temporal shifts in prey availability will be reflected in changes in seal habitat use and foraging patterns. On-going studies of grey seal diet from the Blasket Island colony points to both seasonal and inter-annual variability in seal diet and prey abundance with seals alternating between prey groups depending on which are locally and seasonally available (in review/ [START_REF] Houle | Effects of seal predation on a modelled marine fish community and consequences for a commercial fishery[END_REF]. In this study we used data from grey seals tagged in 2009, 2011 and 2012 compared to the study by Jessopp et al. (2013), which was limited to a single year. Inter-annual and individual variability could also explain differences in foraging associations over different sediments.

Grey seal diet also varies regionally (refs) indeed demersal gadoids feature predominantly in grey seal diet at the Wexford colony, predominantly Trisopterus sp. but also commercial species such as haddock/Pollock/saithe and whiting, and sandeel was found to be of minor importance (Cronin et al., 2012). Considering the apparent differences in sandeel importance in grey seal diets between the southwest and southeast Ireland, sandeel importance to grey seal diets may well be site specific. Indeed [START_REF] Hauksson | Comparative Feeding of Grey (Halichoerus grypus) and Common Seals (Phoca vitulina) in Coastal Waters of Iceland, with a Note on the Diet of Hooded (Cystophora cristata) and Harp Seals (Phoca groenlandica)[END_REF] found that sandeel importance ranged from 18.2% to 91.6% of prey biomass consumed by grey seals at various sites around the coast of Iceland. Sandeels are one of the most important prey groups to grey seals around Scotland (Hammond et al., 1994;Prime and Hammond, 1990) where grey seals show an avoidance of mud (Aarts et al., 2008;Boulcott et al., 2007), yet in northwest France Ridoux et al. (2007) found that sandeels represented a similar proportion of the grey seal diet as was found in southeast Ireland (Luck, 2013). In Western France, grey seals selected rocky areas (Huon et al., 2015) favoured by grey seals' main prey species in this area, wrasse (Ridoux et al., 2007). The differences in sediment preference between Ireland and the other grey seals colonies in the Northeast Atlantic are most likely linked to the differences of habitat and prey availabilities.

Two other environmental factors (distance from the shore and tidal current) were included in the model to add information on foraging habitat selection based on their potential influence on seal orientation and movement. The distance from shore had a low influence on habitat selection in the Irish Sea because of the site configuration (particularly for the Irish Sea, where the area is "closed", because Irish coasts are just in front of coasts of UK). However, we observed a positive influence of this covariate beyond 200 Km on the continental shelf. An area of foraging can explain this fact and was identified close to the limit of the continental shelf. The continental shelf edge may provide productive foraging grounds for higher predators due to upwellings associated with the shelf edge (ref). The influence of 'distance from shore' on foraging habitat selection of grey seals in the Iroise Sea (France) suggests that grey seals select habitat close to the shore potentially to take advantage of the high productivity created by the thermal fronts in this region (Huon et al., 2015). Tidal current had a positive influence on grey seal foraging habitat selection in both the Irish Sea and continental shelf, as seals selected areas with high tidal flow to forage. Tidal currents are generally higher in the Irish Sea compared to the continental shelf, but in both areas seals selected the areas of highest flow for foraging. Tidal current has an influence on habitat selection of many marine organisms including top predators as seals (Zamon, 2003(Zamon, , 2001) ) which may be associated with greater or more predictable prey availability, and increased prey encounter rates.

This is the first study to compare the behavioural plasticity of foraging habitat selection of grey seals across two different ecosystems (Irish Sea/Atlantic Ocean) within a relatively small spatial range. Considering their behavioural plasticity, animals should adjust their behaviour with a trade off between costs and benefits. This behavioural plasticity varies with the environmental conditions. Our findings suggest that foraging habitat selection of this key marine predator is most likely driven by both physical and biological processes, including distances from haulout and bathymetry, and to a lesser extent distance from shore, sediment type, and tidal current. For environmental variables mainly related to the fact the grey seal is a central place forager and a benthic feeder, the behavioural response of grey seal to the distance from haulout and from bathymetry were the same between the Irish Sea and the continental shelf (and compare to other colonies). However, for environmental influences linked to the habitat availability (i.e. sediments, distance from shore and current), distribution of prey resources, the behavioural plasticity of grey seal was highlighted. The different variables did not have the same influence on the animals tagged. We observed a site-specific adaptation (to the continental shelf, Irish Sea, or other sites cited in the literature). Grey seal is a generalist species, composed by specialist individuals. Each individual, depending of its age, sex and weight, has different physiological requirements influencing the behavioural plasticity.Furthermore, prey availability and possibly interactions with fisheries are shaping foraging ranges of grey seals off the east and west coasts of Ireland. Identifying foraging habitat for protected marine species, like the grey seal, is useful for delineating e.g. offshore special areas of conservation or marine protected areas. However as we have demonstrated, foraging habitat selection of the grey seal is driven by many different factors and varies spatially and temporally. Yet a key factor influencing habitat selection is remarkably consistent across the range of the species in the northeast Atlantic, primarily distance from haulout site (this study, Aarts et al., 2008;Huon et al., 2015;McConnell et al., 1999;Sjöberg and Ball, 2000) and such information can inform future decisions for delineating management areas.

FIGURES AND TABLES

PrefaceI

  did my PhD under the supervision of Dr Florence Caurant and Dr Cécile Vincent at the University of La Rochelle (Centre d'Etudes Biologiques de Chizé -UMR7372) and was funded by the French ministry of research. I started my PhD in October 2013, after my 6-months master thesis conducted at the Sea Mammal Research Unit (St Andrews, UK) under the supervision of Dr Jason Matthiopoulos and his staff, working on the habitat modelling of grey seal in the Iroise Sea (France). This PhD was conducted in the framework of two research projects led by the CEBC in collaboration with local NGOs and two Marine Parks: the first project aimed at tracking grey seals in the Parc Naturel Marin d'Iroise (Iroise Sea), while the second one focused on different aspects of the population status and feeding ecology of grey and harbour seals in the eastern English Channel (project "ECO-PHOQUE", funded by the regional council of "Haut-de-France" region and the Parc naturel marin des Estuaires Picards et de la mer d'Opale). For this PhD, I worked in close collaboration with two laboratories outside France, allowing me to work on extended data from seal trackings outside French waters: the Sea Mammal Research Unit (University of Saint Andrews, Scotland, UK) and the MaREI Centre (University College of Cork, Ireland) with Dr Bernie McConnell, Pr Phil Hammond and Dr Michelle Cronin, Dr Mark Jessopp respectively. During my PhD, I spent 3 months outside France in total (in either of these two laboratories), which allows me to apply for the European label. During this PhD, I worked in collaboration with Yann Planque, who developed the vertical and horizontal approaches aiming at the identification of the seals' foraging behaviour. I used these two approaches during my PhD. Yann's work led to a publication: Horizontal and vertical approaches for harbour and grey seals (in prep, ANNEXE II), where I am co-author.

  Photo: Yann Planque

Figure - 1

 1 Figure -1.2-1: The link between spatial and temporal scales of habitat selection. Spatial unit reflect organism mobility (from: Mayor et al. 2008)

Figure - 1

 1 Figure -1.5-1: Representation of Optimal Foraging Theory for prey selection (from Orians and Pearson, 1979)

Figure

  Figure 1.6-1: General diagram summarizing foraging habitat selection of a central place forager species

Figure

  Figure 2.1-1: Grey and harbour seal numbers (orders of magnitude) in Northeast Atlantic

  species in several areas of the Northeast Atlantic. My work focuses at two different scales: the colony scale (on both species) and the individual scale (on grey seals only, using distinct haulout areas during their annual cycle). Since marine environmental conditions differ along the different coasts, and also because of the different trends in abundance in the different colonies, my work focused on distinct areas around the British Isles. I aim at studying the spatial usage and habitat selection by both species at a local scale around each colony, depending on the habitat availability and the influence of intra and inter-specific interactions, as well as the individuals or species' behavioural plasticity. As grey seals move between different colonies, I studied their individual strategies of habitat selection following the different pressures encountered around the colonies they visit. Five study areas were selected at the colony scale, representing contrasted areas with specific habitat and prey availability and colony dynamics: the Irish continental shelf; the Irish Sea; the Inner Hebrides; the North Sea; the Iroise Sea and the English Channel (Figure 2.2-1).

Figure 2

 2 Figure 2.2-1: Map of study areas and tagging sites (map projection: Lambert 93)

Figure 2

 2 Figure 2.5-1: Female B32's tracking assessment; the under-strip represent the percentage of time spent hauling out (green), diving (yellow), at the surface (blue)

Figure 2

 2 Figure 2.6-1: General map of grey seal tracks in all study areas (map projection: Lambert 93)

Figure 2

 2 Figure 2.9-1: Behavioural states of a predator during its trip in a heterogeneous habitat

  bathymetry dataset was obtained from the European Marine Observation and Data Network (EMODnet) * including bathymetric data from survey datasets (corresponding to GEBCO database) and composite Digital Terrain Model (DTM). GEBCO dataset corresponds to the 30° gridded data; and the DTM has a spatial resolution of 1/8 of arc-minutes. The general resolution of grid size is 0.125*0.125 minutes. The bathymetry dataset covers the general study area including the Iroise Sea, the English * http://portal.emodnet-bathymetry.eu/ Channel, the North Sea, the Irish Sea and the Irish Continental Shelf. The extent of bathymetry is presented Figure 2.11-1.

Figure 2 .

 2 Figure 2.11-1 Map of bathymetry in the Northeast Atlantic (map projection: Lambert 93)

Figure 2 .

 2 Figure 2.11-2: Map of Sediment types repartition (map projection: Lambert 93)

AFigure

  Figure 2.11-3: Map of tidal current speed in France (A); Ireland (B); Scotland (C); (maps projection: Lambert 93)

Figure

  Figure 3.1-1: Map of the Iroise Sea (map projection: Lambert 93)

AFigure

  Figure 3.1-2: Foraging areas from foraging dive locations (A) and ARS locations (B); and all at sea locations (C); (maps projection: Lambert 93)

  Figure 3.2-1: GAMM smoothing curves for the Distance from the last haulout (Km), the bathymetry (m), and the tidal current speed (m/s) for the (A) habitat selection; (B) horizontal approach; (C) vertical approach; grey shadings indicate 95% confidence intervals for model prediction

AFigure 3

 3 Figure 3.2-2: Maps of GAMM predicted values of habitat selection (A); foraging habitat selection with horizontal approach (B); foraging habitat selection with vertical approach (C); (maps projection: Lambert 93)

Figure 4 .

 4 Figure 4.1-1: Log trip duration (hours) for grey seals tracked in the Firth of Tay (purple); in the Iroise Sea (turquoise); in Eastern English Channel (green); in the Irish Continental Shelf (orange); and in the Irish Sea (yellow)

Figure 4 .

 4 Figure 4.1-2: Log trip maximum extent for seals tracked in the Firth of Tay (purple); in the Iroise Sea (turquoise); in the Eastern English Channel (green); in the Irish Continental Shelf (orange); in the Irish Sea (yellow)

Figure

  Figure 4.2-1: B31's Kernel density of foraging dives representing foraging areas in the Iroise Sea (A) and in the Irish Continental Shelf (B)

Figure 4 .

 4 Figure 4.2-2: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); and for the tidal current speed (m/s) for B31 in the Iroise Sea; grey shadings indicate 95% confidence intervals for model prediction

Figure 4 .

 4 Figure 4.2-3: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from shore (Km); tidal current speed (m/s) for B31 in the Irish Continental Shelf; grey shadings indicate 95% confidence intervals for model prediction

Figure

  Figure 4.2-4: B37's Kernel density of foraging dives representing foraging areas in the Iroise Sea (A) and around the Cornwall (B); (maps projection: Lambert 93)

Figure 4 .

 4 Figure 4.2-5: GAMM smoothing curves for the distance from the last haulout (Km) and the tidal current speed (m/s) for foraging habitat selection of B37 in the Iroise Sea; grey shadings 95% confidence intervals for model prediction

Figure 4 .

 4 Figure 4.2-6: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); tidal current speed (m/s) of B37 in Cornwall; grey shadings indicate 95% confidence intervals for model prediction

Figure

  Figure 4.2-7: G04's Kernel density of foraging dives representing foraging areas in the Eastern English Channel (A) and in Wadden Sea (B); (maps projection: Lambert 93)

Figure 4 .

 4 Figure 4.2-8: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); tidal current speed (m/s); distance from shore (Km) of G04 in Eastern English Channel, grey shadings indicate 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-9: GAMM smoothing curves for the distance for the last haulout (Km); bathymetry (m); tidal current speed (m/s); distance from shore (Km) of GO4 in the Wadden Sea; grey shadings indicate 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-11: GAMM smoothing curves for the distance from the last haulout (km); bathymetry (m); tidal current speed (m/s); distance from the shore (Km) for G11 in the

Figure 4

 4 Figure 4.2-12: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); tidal current speed (m/s) for G11 in the North Sea; grey shading indicates 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-13: Kernel density of foraging dives representing foraging areas in the Eastern English Channel (map projection: Lambert 93)

Figure 4 .

 4 Figure 4.2-14: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from shore (Km); tidal current speed (m/s) for grey seals in the Eastern English Channel; grey shading indicates 95% confidence intervals for model prediction

Figure 4 .

 4 Figure 4.2-15: Map of GAMM predicted values of foraging habitat selection of grey seals in the Eastern English Channel (map projection: Lambert 93)

Figure

  Figure 4.2-16: Irish Continental Shelf foraging dives representing foraging areas (map projection: Lambert 93)

(

  . Foraging habitat selection decreased gradually from 0 to 250 Km from the last haulout, and decreased rapidly over 250 Km. Bathymetry had a negative influence on foraging habitat selection. Distance from shore seemed to have a positive influence on foraging habitat selection. There is a slow increase of foraging habitat selection from 0 to 200 Km and a quick increase over 200 Km. Tidal current had a positive influence on the habitat selection for tidal current speed up to 0.15m/s, and then had a negative influence. Seals also selected foraging habitats with coarse sediments, avoiding sandy and muddy areas. The influence of mixed sediments and rocky areas was not significant (p>0.05).The foraging habitat selection was mainly located around Galway Bay, and to a lesser extent close to the Continental Shelf (selection value of 0.3-0.6, Figure4.2-18).

Figure 4 .

 4 Figure 4.2-17: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from shore (Km); tidal current speed (m/s) for grey seals in the Irish Continental Shelf area; grey shadings indicate 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-19: Kernel density of foraging dives locations representing foraging areas of grey seals in the Eastern English Channel (map projection: Lambert 93)

Figure 4

 4 Figure 4.2-20: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from shore (Km); tidal current speed (m/s) for grey seals foraging habitat selection in the Irish Sea; grey shadings indicates 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-22: Kernel density of foraging dives representing foraging areas of grey seals in the Firth of Tay (map projection Lambert 93)

Figure 4

 4 Figure 4.2-23: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m), for the tidal current speed (m/s) for grey seals in the Firth of Tay; grey shadings indicate 95% confidence intervals for model prediction

Figure 4

 4 Figure 4.2-24: Map of GAMM predicted values of habitat selection for grey seals in the Firth of Tay (map projection: Lambert 93)

  highlighted a foraging behaviour that was probably developed in other areas (i.e. no maternal learning). This behaviour is similar to the one observed on grey seals in the North Sea, traveling further on specific offshore bank. Grey seals in the Eastern English Channel come from other colonies from UK and Netherland, where seals forage in the North Sea and might adopt this behaviour. Furthermore, the number of grey seals is low compared to other colonies in the North Sea, limiting intra-species competition. Do grey seals coming from other colony in the North Sea adapt their behaviour to the habitat availability or do they use the same behaviour? To answer to this question, I studied the local foraging habitat selection of some grey seals tagged in the Iroise Sea and in the Bay of Somme, to see if they adopt the same behaviour when they move to other regions. Several studies have shown an adaptation of the animals when they change environments with different biotic and abiotic factors, by adapting their behaviour and habitat selection.

Figure 5

 5 Figure 5.1-1: Log trip duration for harbour seals tracked in the Firth of Tay (purple); in the Kenmare Bay (turquoise); in the Baie du Mont Saint Michel (green); in the Baie de Somme (orange); in the Baie des Veys (yellow); and in the Inner Hebrides (brown)

Figure 5

 5 Figure 5.1-2: Log of trip maximum extent for harbour seals tracked in the Firth of Tay (purple); in the Kenmare Bay (turquoise); in the Baie du Mont Saint Michel (green); in the Baie de Somme (orange); in the Baie des Veys (yellow); and in the Inner Hebrides (brown)

Figure 5

 5 Figure 5.2-1: Kernel densities of foraging dives representing foraging areas in the Baie du Mont Saint Michel and in the Baie des Veys (map projection: Lambert 93)

  Figure 5.2-3 The foraging habitat selection of harbour seals in the Baie du Mont Saint Michel is mainly located on the East part of the Bay.

Figure 5

 5 Figure 5.2-2: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from shore (Km); tidal current speed (m/s) for harbour seals foraging habitat selection in the Baie du Mont Saint Michel; grey shadings indicate 95% confidence intervals for model prediction

Figure 5

 5 Figure 5.2-3 shows the map of foraging habitat selection of harbour seals in the Baie des Veys. In this area, harbour seals mainly selected their foraging habitat within the estuarine part of the Baie des Veys, and in the East part of this study area.

Figure 5

 5 Figure 5.2-4: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); distance from the shore (Km); tidal current (m/s) for harbour seals habitat selection in the Baie des Veys; grey shadings indicate 95% confidence intervals for model prediction

Figure 5

 5 Figure 5.2-5: Kernel densities of foraging dives locations representing foraging areas of harbour seals in the Baie de Somme (map projection: Lambert 93)

Figure 5 .

 5 Figure 5.2-6: GAMM smoothing curves for the distance from the last haulout (Km); distance from shore (m); tidal current speed (m/s) for the foraging habitat selection of harbour seals in the Baie de Somme; grey shadings indicate 95% confidence intervals for model prediction

Figure 5

 5 Figure 5.2-8: Kernel densities of foraging dives locations representing foraging habitat selection of harbour seals in the Kenmare Bay

Figure 5 .

 5 Figure 5.2-9: GAMM smoothing curves for the Distance from the last haulout (km); and distance from shore (Km); for foraging habitat selection of harbour seals in the Kenmare Bay; grey shadings indicate 95% confidence intervals for model prediction

Figure 5 .

 5 Figure 5.2-11: Kernel densities of foraging dives locations representing foraging areas of harbour seals in the Inner Hebrides (map projection: Lambert 93)

Figure 5 .

 5 Figure 5.2-12: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); tidal current speed (m/s); for harbour seal foraging habitat selection in the Inner Hebrides; grey shading indicates 95% confidence intervals for model prediction

Figure 5 .

 5 Figure 5.2-14: Kernel densities of foraging dive locations representing foraging activities of harbour seals in the Firth of Tay (map projection: Lambert 93)

Figure 5 .

 5 Figure 5.2-15: GAMM smoothing curves for the distance from the last haulout (Km); bathymetry (m); tidal current speed (m/s); of habitat selection of harbour seals in the Firth of Tay; grey shadings indicate 95% confidence intervals for model prediction

  explained 33% of the habitat selection of gannet. The explained deviances for the Baie du Mont Saint Michel, Baie des Veys, Baie de Somme, Kenmare Bay, Inner Hebrides and Firth of Tay were 62.9%, 70.3%, 77.3%, 60.1%, 75.1% and 58.9% respectively. The variables included in the model succeeded in capturing an important part of the variability, explaining thus an important part in the foraging habitat selection of harbour seals.As harbour seals were very inshore in the six study areas, I detected colinearity between the distance from the last haulout, distance from the shore and the bathymetry in most of the cases. Most of the time two of these three variables explained an important part of the deviance (except for the Firth of Tay, where the tidal current explained most of the model deviance). The distance from the last haulout, distance from shore and bathymetry had a negative influence on the selection but at different degrees, depending of the site configurations. These results were in agreement with what the literature highlighted about the harbour seal habitat use. The harbour seal is known to be a coastal animal diving in shallow waters. In the Saint-

  sites. The other tested parameters constituted on average a much lower percentage of the model's explained deviance. The selection of foraging habitat corresponded to the habitat of the prey species targeted by the seals at a local scale. For both species, the results also highlighted the influence of habitat availability (therefore the indirect influence of the prey availability) and intra-specific interactions (i.e. the effect of density dependence) on the spatial usage and foraging habitat selection at the local scale. These percentage in the explained deviance should be treated as orders of magnitude rather than exact figures, however they clearly indicate the importance of the explanatory variables in the animals' choice in their habitat use. In most published studies on habitat modelling of central place forager, the part of the distance from the last haulout (or from colony) in the model's deviance is not provided, preventing further comparison of my results to the literature. Different hypotheses can be given to explain the importance of the distance from the last haulout for the habitat selection.

Figure 6 .

 6 Figure 6.2-1: Boxplot of Bhattacharyya Index for harbour seals (Pv) individual pairscomparison; grey seals (Hg) pairs-comparison; Species (Pv/Hg) pairs-comparison for the Eastern English Channel (in blue) and the Firth of Tay (in pink)

Figure 1 .

 1 Figure 1. (A) Map of all seal movements (travel and return trips) in 2010-2013 from their capture site in the Iroise Sea marine park, France and (B) magnified map of the marine park area (dotted lines) showing return-trip tracks only, from the haulout sites located in the Iroise Sea. The main capture site is also shown (star).

Figure 2 .

 2 Figure 2. Study area including real track points selected every 200 min (in black) and associated random points (in gray) for gray seals in the Iroise Sea, 2010-2013.

Figure 3 .

 3 Figure 3. Effect of environmental factors on gray seal spatial selection in the Iroise Sea, 2010-2013: (A) distance to haulout (m), (B) bathymetry (m), (C) distance from shore (m), and (D) tidal current (m/s). Dotted lines indicate 95% confidence intervals for model prediction.

Figure 4 .

 4 Figure 4. Habitat selection of gray seals within the Iroise Sea marine park, France, 2010-2013.

Equation

  of the maximum dive trapezoid covered by the dive DiveDur = Dive duration MaxDepth = Maximum depth of the dive (the value of 1.5m is subtracted to this maximum depth as the tag detects a dive only below this threshold) S = Vertical travel speed, estimated MCTS

Figure 2 -

 2 Figure 2 -Example of three seals' trips illustrating the process of Area-Restricted Search (ARS) behaviour assessment by the use of First Passage Time (FPT) analysis (horizontal approach). FollowingFauchald and and Tveera (2003), the first step consists in the detection of the most representative spatial scale r (among the different circle radius tested) illustrating an ARS behaviour, which corresponds to the maximum of the variance of the log-transformed FPT values, S(r) (top). The second step is characterized by the selection of the trips parts associated with an ARS behaviour by applying[START_REF] Lavielle | Detection of multiple changes in a sequence of dependent variables[END_REF][START_REF] Lavielle | Using penalized contrasts for the change-point problem[END_REF] segmentation process (middle). The maps show the GPS locations in ARS along the three trips (bottom).

Figure 3 -

 3 Figure 3 -Influence of the number of GPS locations per trip on the probability to detect an ARS along these trips for harbour seals (dark line) and grey seals (light line). Solid lines correspond to the prediction of logistic models and shaded regions around them represent the 95% confidence limits for the estimated prediction. Dotted lines indicate a probability of 0.95.

Figure 4 -

 4 Figure 4 -U-shaped dives selection process based on the distribution of individual dives according to their shape (first criterion of vertical approach). This process is presented here for two grey seals (left) and one harbour seal (right). The dotted lines indicate the thresholds selected for each individual to select at least 25% of their more U-shaped dives.

Figure 6 -

 6 Figure 6 -Grey and harbour seals' foraging areas as assessed from the horizontal (left) and vertical approaches (right). The foraging areas are represented on grids of 5x5 km cells and characterized by the spatial density of GPS locations in Areas of Restricted Search (ARS) (horizontal approach) and by foraging dives locations (vertical approach). Core to extended foraging areas are also represented from 75% Kernel densities of foraging locations (black lines).

  Estimated Minimum Coast of Transport Speed (S) used to assess dive shape, and determination of the two foraging criteria to select U-shaped (TAD threshold) excluding those probably associated with resting or sleeping while diving (vertical descent speed threshold) (vertical approach). The different values were assessed for each individual.

Fig. 1 .

 1 Fig. 1. Map of all grey seal (red) and harbour seal (green) haulout sites in metropolitan France. Circles indicate haulout sites where the seasonal maximum number of seals exceeds 50 individuals. Stars indicate smaller haulout sites used by fewer seals, not detailed in this study. Symbols surrounded by thick, black circles show the seal colonies where telemetry was conducted. Marine Protected Areas are also shown, including Special Areas of Conservation and Marine National Parks. Nature Reserves are not visible but also encompass some haulout sites, in SEP, BDS and BDV for instance. Haulout sites are: Molene archipelago (MOL), Sept iles archipelago (SEP), baie du Mont-Saint-Michel (BSM), baie des Veys (BDV), baie de Somme (BDS), baie d'Authie (BDA) and Walde (WAL) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 2 .

 2 Fig.2. Seasonal variations in grey seal counts at haulout sites, according to quarters of the year. Up to one maximum count per month is included for years 2013, 2014 and 2015 (see Table1for the number of counts per site and species). Quarter 1=January to March; Quarter 2=April to June; Quarter 3=July to September; Quarter 4=October to December.

  six seals hauling out in both Southwest UK and Ireland. Most of these movements occurred close to the breeding season. Breeding colonies used by the seals tracked from MOL included Islay (Scotland), Inishark and the Blasket islands (Ireland), the Isles of Scilly (England), and SEP and MOL (France) (Fig. 10; breeding behaviour assessed from Figs. 3S

Fig. 3 .

 3 Fig. 3. Seasonal variations in harbour seal counts at haulout sites, according to quarters of the year. Up to one maximum count per month is included for years 2013, 2014 and 2015 (see Table1for the number of counts per site and species). Quarter 1=January to March; Quarter 2=April to June; Quarter 3=July to September; Quarter 4=October to December.

Fig. 4 .

 4 Fig. 4. Maximum yearly counts (dots) of harbour seals and associated trends (lines) in the four main study sites.

Fig. 5 .

 5 Fig. 5. Maximum yearly counts (dots) of grey seals and associated trends (lines) in the four main study sites.

Fig. 6 .

 6 Fig. 6. Density of harbour seal locations (per grid cell) obtained by telemetry from 2006 to 2010, from individuals captured in BSM, BDV and BDS.

Fig. 8 .

 8 Fig. 8. Densities of seal observations (per grid cell) from the aerial surveys conducted in 2012 and 2014: absence of transect is shown by the absence of grid cells while seal densities are given as a number of seals observed in a given grid divided by the number of flight passages per grid cell. The seal species are not detailed in these aerial surveys.

Fig. 9 .

 9 Fig. 9. Harbour seal telemetry tracks from BSM (6 individuals tracked in 2006 and 2007, in purple), BDV (12 individuals tracked in 2007 and 2008, in blue) and BDS (10 individuals tracked in 2010, in orange). Red dots indicate haulout locations of the seals. Seals tracked for less than a month are not shown here (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 10 .

 10 Fig. 10. Grey seal telemetry tracks from MOL (15 individuals tracked by Argos tags from 1999 to 2003, in light blue, and 19 individuals tracked by GPS/GSM tags from 2010 to 2013, in dark blue) and BDS (11 individuals tracked in 2012, in green). Red dots indicate haulout locations of the seals. Thick, red circles indicate breeding locations, as suggested from the activity budget of the seals (see Figs. 3S and 4S, supplementary data) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.).

Fig. 11 .

 11 Fig. 11. Schematic view of the suggested horse-shoe distribution pattern of grey seals around the British Isles. Grey seals' real distribution is wider than the orange shape shown here. Black arrows indicate areas of recent grey seal recolonization and/or high rates of increase in the southern North Sea and eastern Channel.

  Foraging habitat selection decreased gradually from 0 to 250 Km from the last haulout, then decreased rapidly after 250 Km. Bathymetry had a negative influence on foraging habitat selection: grey seals selected shallow waters. Distance from shore seemed to have a positive influence on foraging habitat selection. There is a slow increase from 0 to 200 Km and a quick increase of foraging habitat selection after 200 Km. Grey seals on the continental shelf selected a tidal current of 0.15m/s. They also avoided habitat with muddy and sandy sediments, selecting rocky habitat. The foraging habitat selection was mainly located around Galway Bay; a small area of foraging habitat is observed close to the continental shelf (a relatively low selection value between 0.3 and 0.6) (Fig 8).

  How higher marine predators select their habitat is governed by both intrinsic and extrinsic factors, including resource availability and environmental variability. Physical or environmental factors (including water depth, sediment type, tidal currents) may influence the availability of food and habitat (at least in proximity to colonies in central place foragers), influencing consequently the behavioural plasticity of animal. In this study of a central place forager (the grey seal), we examined the behavioural plasticity of two colonies of grey seals in centred habitats in Ireland (continental shelf and Irish Sea) subject to different environmental pressure. First, we used trip characteristics and secondly, foraging habitat selection. We used for the first time foraging dives to study foraging habitat selection by grey seals.

Figure 1 :Figure 3 :Figure 4 :Figure 5 :

 1345 Figure 1: Western and Eastern Seaboard study areas (respectively blue and green dotted lines) including main colony sites (stars) and tagging sites (red stars) for grey seals tagged in Ireland

Figure 7 :Figure 8 :

 78 Figure 7: Effect of environmental factor on grey seal habitat selection: distance to haulout (Km), bathymetry (m), distance from shore (Km) and tidal current (m/s) for Western (A) and Eastern (B) seaboards

εij

	εij: model residuals
	I fitted GAMMs to the data with the gam function (package mgcv) in R (R Core Team
	Where:
	i: point in space (i.e. use or available)
	j: seal
	Yij: represents the response variable
	X1,2…,k: represents the covariates included as fixed effect
	S 1,2,…,k: represents the smooth functions of covariates Xk
	μj: random effect

Table 3 .2-1: Models carried for all locations; the vertical approach (foraging dive locations); the horizontal approach (ARS locations); Deviance explained (DE); smoothing term (s); model selected for each approach is in bold

 3

	Input	Model

selection ≈ s(dist_HL) + s(bathy) + factor(sediment) + s(tidal_current) + s(ref, bs="re)

	Foraging dives	locations
			41,487 0.00	79.3%
		UAD selection ≈ s(dist_HL) + s(dist_shore) +	43,274 1,787	78.5%
		factor(sediment) + s(tidal_current) + s(ref,
		bs="re)

UAD selection ≈s(dist_HL) + s(bathy) +factor(sediment) + s(tidal_current) + s(ref, bs="re)

		38,544 0.00	78.3%
	UAD selection ≈ s(dist_HL) + s(dist_shore)	39,264 171	78.5%
	+factor(sediment) + s(tidal_current) + s(ref,
	bs="re)
	UAD
	ARS locations

selection ≈ s(dist_HL) + s(bathy) + factor(sediment) + s(tidal_current) + s(ref, bs="re)

		4,919	0.00	80.6%
	UAD selection ≈s(dist_HL) + s(dist_shore) +	5,090	171	77.9%
	factor(sediment) + s(tidal_current) + s(ref,
	bs="re)

Table 3 .2-2: Results of the GAMM model for habitat selection in the Iroise Sea using all locations Smoother term edf Chi-square P-value

 3

	Distance from last haulout	2.88	18,217	<0.0001
	Bathymetry	2.98	3,198	<0.0001
	Current	2.98	195	<0.0001
	Seal	8.98	3,544	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept	-2.66	-8.85	<0.0001
	Mixed-sediment	0.13	3.42	<0.001
	Mud	-1.23	-13.71	<0.0001
	Rock	-0.08	-2.13	0.035
	Sand	-0.40	-9.26	<0.0001

Table 3 .2-3: Results of the GAMM model for foraging habitat selection in the Iroise Sea using the horizontal approach Smoother term edf Chi-square P-value

 3

	Distance from last haulout	2.974	1,666	<0.0001
	Bathymetry	2.984	328	<0.0001
	Current	2.993	148	<0.0001
	Seal	8.869	760	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept	-71.55	-6.32	<0.0001
	Mixed-sediment	0.83	7.64	<0.0001
	Mud	-1.01	-3.82	<0.001
	Rock	0.42	3.39	<0.001
	Sand	-0.13	-0.98	0.33

Table 3 .2-4: Results of the GAMM model for foraging habitat selection in the Iroise Sea using the vertical approach Smoother term edf Chi-square P-value

 3

	Distance from last haulout	2.64	15,288	<0.0001
	Bathymetry	2.99	2,732	<0.0001
	Current	2.993	284	<0.0001
	Seal	8.98	4,075	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept	-2.79	-7.40	<0.0001
	Mixed-sediment	0.44	11.20	<0.0001
	Mud	-1.51	-14.77	<0.0001
	Rock	-0.38	-8.87	<0.0001
	Sand	-0.15	-3.31	<0.001

Table 4

 4

.1-1). The Dunn test was used as a post-hoc test to perform site paircomparisons. The median trip duration was highest in the Irish Sea

(23.91 ± 48.48 hours)

and shortest in the Eastern English Channel (6.85 ± 53.75 hours). The median trip duration in the Firth of Tay (7.21± 92.77 hours) was significantly different from the other areas. In the Iroise Sea and the Irish continental shelf, trip durations were not significantly different (9.56 ± 16.41 hours and 8.93 ± 35.68 hours respectively) but they were different from the other areas (Figure

4

.1-1). The intra site variability (i.e.

inter individual variability within each study area, Table

4

.1-1) is strong for all areas, with 32.75 to 64.06 % of possible pair comparisons being significantly different. The Iroise Sea exhibited the highest individual variability (41 pairs significantly different among 64 comparisons, i.e. 64.1%), and the Firth of Tay, the lowest (19 pairs significantly different on 58 comparisons, i.e. 32.8%). However the inter-site variability of trip duration was highest than the intra-site one (Figure

4

.1-1).

Table 4 .1-1: Summary of individual-pairs (in red; nb of significantly different pairs/total nb); and site-pairs (pvalue) comparison with Dunn test of grey seals' trip durations; Iroise Sea, Eastern English Channel

 4

		Iroise Sea	E.E.C.	I.C.S	Irish Sea	FoT
	Iroise Sea	41/64	<0.05	0.07	<0.05	<0.05
	E.E.C		32/52	<0.05	<0.05	0.02
	I.C.S			24/64	<0.05	<0.05
	Irish Sea				26/52	<0.05
	FoT					19/58

(E.E.C), Irish Continental Shelf (I.C.S), Irish Sea, Firth of Tay (F.o.T)

Table 4

 4 different according to the area (Table4.2-1). The behavioural variability between individuals was the highest for the Irish Sea. (31 pairs significantly different on 52 comparisons, i.e. 59.6%), and the lowest rate was observed in the Eastern English Channel (20 pairs significantly different on 52 pairs comparison,

.1-2). A post hoc test was used to do site-pair comparisons. The distribution of trip extents was always significantly different between sites (p<0.05, Figure

4

.1-2), except for the Firth of Tay -Eastern English Channel pair (p=0.0945). The median maximum extent was the highest for the firth of Tay (15.01 ± 79.99 Km) and the lowest for the Irish Continental Shelf (1.87 ± 43.38 Km). The medium maximum extent for the Iroise Sea, the Eastern English Channel and the Irish Sea were respectively 8.

09 ± 17.19 Km, 14.66 ± 47.90 Km and 9.05 ± 27.77

Km. The behavioural variability between individuals was also highlighted with 38.5% to 59.6 % of possible individual-pair comparisons being significantly i.e. 38.5%). The inter-site variability is shown in spite of the intra-site variability (Figure

4

.1-2).

Table 4 .1-2: Summary of individual pairs (in red; nb of significantly different pairs/total nb) and site-pairs (pvalue) comparison with Dunn test for the grey seals' trips maximum extent in the Iroise Sea, Eastern English Channel

 4

		Iroise Sea	E.E.C.	I.C.S	Irish Sea	FoT
	Iroise Sea	35/64	<0.05	<0.05	<0.05	<0.05
	E.E.C		20/52	<0.05	<0.05	0.09
	I.C.S			31/64	<0.05	<0.05
	Irish Sea				31/52	<0.05
	FoT					30/58

(E.E.C), Irish Continental Shelf (I.C.S), Irish Sea and Firth of Tay (F.o.T)

Table 4 .2-1: Models of foraging habitat selection for B31 in the Iroise Sea; and in the Irish Continental Shelf; Deviance Explained (DE); Used Availability Design (UAD); model selected is in bold

 4

	Site	Model

factor(sediment) + s(tidal_current) + s

(trip, 122,445 113,401 48.4%

bs="re") Irish Cont. Shelf

UAD selection ≈ s(dist_HL) + s(bathymetry)

+ factor(sediment) + s(tidal_current) + s(trip, bs="re")

Table 4 .2-2: Results of GAMM model for the foraging habitat selection of B31 in the Iroise Sea

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.97	1,736	<0.0001
	Current	2.99	1,982	<0.0001
	Seal	0.83	4.89	0.02
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-7.52	-8.99	<0.0001
	Coarse-sediment	-0.16	-1.93	0.05
	Mixed-sed / Rock	-0.61	-4.28	<0.0001
	Mud	-1.69	-8.01	<0.0001

Table 4 .2-3: Results of the GAMM model for the foraging habitat selection of B31 in the Irish Continental Shelf

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.90	99.62	<0.0001
	Bathymetry	2.99	109.35	<0.0001
	Current	2.93	47.82	<0.001
	Distance from shore	2.99	113.95	<0.0001
	Random effect	0.95	22.65	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse sed)	-5.41	-8.60	<0.0001
	Mixed sediment	-0.14	-0.37	0.71
	Mud	-2.00	-2.55	0.01
	Rock	0.29	1.12	0.26
	Sand	0.63	3.68	<0.001

Table 4 .2-4: Models of foraging habitat selection of B37 in the Iroise Sea and in Cornwall; Deviance Explained (DE); Use Availability Design (UAD)

 4

	Site	Model

factor(sediment) + s(tidal_current) + s(trip, bs="re") 7,033 1,519 63.0% Cornwall UAD selection ≈ s(dist_HL) + s(bathymetry) + factor(sediment) + s(tidal_current) + s(trip, bs="re")

Table 4 .2-5: Results of the GAMM model for the foraging habitat selection of B37 in the Iroise Sea

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.97	1,736	<0.0001
	Current	2.99	1,982	<0.0001
	Seal	0.83	4.89	0.02
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-7.52	-8.99	<0.0001
	Coarse-sediment	-0.16	-1.93	0.05
	Mixed-sed / Rock	-0.61	-4.28	<0.0001
	Mud	-1.69	-8.01	<0.0001

Table 4 .2-6: Results of the GAMM model for B37 foraging habitat selection in the around Cornwall

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	1,436	<0.0001
	Bathymetry	2.59	203.52	<0.0001
	Current	2.87	325.27	<0.0001
	Random effect	0.98	22.16	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse-sed.)	-48.22	-9.19	<0.0001
	Mixed-sediment	-0.79	-1.82	0.07
	Mud	-0.66	-1.73	0.08
	Rock	-0.47	-3.72	<0.001
	Sand	-0.99	-8.05	<0.0001

Table 4 .2-7: Results of the GAMM model for the G04 foraging habitat selection in the Eastern English Channel

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	1436.12	<0.0001
	Bathymetry	2.59	203.52	<0.0001
	Current	2.87	325.27	<0.0001
	Distance from shore	2.99	447.35	<0.0001
	Random effect	0.98	22.16	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse sed.)	-1.81	-22.08	<0.0001
	Mixed sediment	-0.47	-0.99	0.32
	Mud	-3.4e-06	0.00	1.00
	Rock	1.30	14.36	<0.0001
	Sand	0.51	4.40	<0.0001

Table 4 .2-8: Results of the GAMM model for G04 foraging habitat selection in the Wadden Sea

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	1,695	<0.0001
	Bathymetry	2.99	829.17	<0.0001
	Current	1.00	5.72	0.02
	Distance from shore	2.99	182.12	<0.0001
	Random effect	0.97	29.84	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-85.78	-15.64	<0.0001
	Coarse sediment	1.02	9.40	<0.0001
	Mixed	-48.83	0.00	1
	Mud	-38.83	0.00	1

Table 4 .2-9: Results of the GAMM model for the G11 foraging habitat selection in the Eastern English Channel

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	516.61	<0.0001
	Bathymetry	2.99	246.01	<0.0001
	Current	2.99	315.66	<0.0001
	Distance from shore	2.99	405.93	<0.0001
	Random effect	0.93	13.27	<0.001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse-sed.)	-5.94	-10.80	<0.0001
	Mud	0.07	0.24	0.81
	Rock	0.07	0.51	0.61
	Sand	-0.11	-0.67	0.50

Table 4 .2-10: Models of foraging habitat selection for G11 in the North Sea: Deviance Explained (DE); Use Availability Design (UAD), model selected is in bold

 4

	Site	Model

selection ≈ s(dist_HL) + s(bathymetry) + factor(sediment) + s(tidal_current) + s(trip, bs="re")

		18,488	265	18.7%
	UAD selection ≈ s(dist_shore) + s(bathymetry) +	18,223	0.0	19.8%
	factor(sediment) + s(trip, bs="re")
	Foraging habitat was selected either close to the haulout site, or more than 200 km
	away from it. Foraging habitat selection decreased when bathymetry exceeded 40m. It
	also decreased rapidly for tidal current speed decreasing from 0 to 0.35m/s, then

increased. Seal G11 avoided mud, sand and coarse sediments, but the influence of rock was not significant (p>0.5)

Table 4 .2-11: Results of the GAMM model for G11 foraging habitat selection in the North Sea

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.83	722.89	<0.0001
	Bathymetry	2.99	516.98	<0.0001
	Current	2.99	791.66	<0.0001
	Random effect	0.99	70.83	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse sed.)	-1.07	-10.50	<0.0001
	Mixed sediment	-0.49	-1.11	0.27
	Mud	0.05	0.57	0.57
	Sand	0.34	5.05	<0.0001

Table 4 .2-12: Models of foraging habitat selection for the Eastern English Channel; Deviance explained (DE); Use Availability Design (UAD); model selected is in bold.

 4

	Model	AIC	Delta	DE
			AIC
	UAD selection ≈ s(dist_HL) +	136,729	0.00	30.9%
	s(dist_shore) + factor(sediment) +
	s(tidal_current) + s(ref, bs="re)
	UAD selection ≈ s(dist_HL) + s(bathy) +	138,918	2,189	27.5%
	factor(sediment) + s(tidal_current) +
	s(ref, bs="re)

Table 4 .2-13: Results of the GAMM model for the grey seals' foraging habitat selection in the Eastern English Channel

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	19,218	<0.0001
	Distance from shore	2.99	2,457	<0.0001
	Current	2.98	3,176	<0.0001
	Seal	6.90	7,206	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (coarse-sed.)	-0.08	-1.74	0.08
	Mixed-sediment	-3.34	-13.79	<0.0001
	Mud	0.51	21.82	<0.0001
	Rock	-1.85	-19.59	<0.0001
	Sand	-1.31	-53.06	<0.0001

Table 4 .2-14: Results of the GAMM model for the foraging habitat selection of grey seals in the Irish Continental Shelf

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.99	7,111	<0.0001
	Distance from shore	2.99	1,372	<0.0001
	Bathymetry	1.00	1,367	<0.0001
	Current	2.99	963.7	<0.0001
	Seal	8.916	804.3	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-12.17	-29.39	<0.0001
	Coarse-sediment	0.55	13.77	<0.0001
	Mixed-sediment	0.11	1.53	0.13
	Mud	-0.275	-2.25	0.03
	Rock	0.114	1.27	0.20

Table 4 .2-15: Results of GAMM model for the foraging habitat selection of grey seals in the Irish Sea

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.98	4,824	<0.0001
	Distance from shore	2.98	410	<0.0001
	Bathymetry	2.51	536	<0.0001
	Current	2.84	127.7	<0.0001
	Seal	6.91	376.9	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-4.96	-12.95	<0.0001
	Coarse-sediment	-0.63	-4.16	<0.0001
	Mixed-sediment	1.44	15.52	<0.0001
	Mud	0.44	2.27	0.02
	Rock	0.77	9.33	<0.0001

Table 4 .2-16: Models of foraging habitat selection for grey seals in the Firth of Tay; Deviance explained (DE); Use Availability Design (UAD)

 4

	Model	AIC	Delta	DE
			AIC
	UAD selection ≈ s(dist_HL) + s(bathy)	156,259	0.0	51.8%
	+ factor(sediment) + s(tidal_current)
	+ s(ref, bs="re)
	UAD selection ≈ s(dist_shore) +	192,206 35,947	55%
	s(bathy) + factor(sediment) + s(ref,
	bs="re)

Table 4 .2-17: Results of the GAMM model for the foraging habitat selection of grey seals in the Firth of Tay

 4

	Smoother term	edf	Chi-square	P-value
	Distance from last haulout	2.85	24,430	<0.0001
	Bathymetry	2.98	21,766	<0.0001
	Current	2.98	2,465	<0.0001
	Seal	7.99	8,773	<0.0001
	Parametric coefficient	Estimate	Zvalue	P-value
	Intercept (sand)	-1.601	-5.64	<0.0001
	Bay	-8.88	-8.87	<0.0001
	Coarse-sediment	1.25	79.46	<0.0001
	Estuary	-2.23	-8.55	<0.0001
	Mixed-sediment	-5.87	-10.09	<0.0001
	Mud	-2.72	25.81	<0.0001
	Rock	-5.89	-9.90	<0.0001
	Shelf trough	-3.94	-4.38	<0.0001

  Baie de Somme (p=0.47) where individuals exhibited a similar distribution in their trip duration. The median trip duration was highest for the Inner Hebrides (18.29 ± 35.03 hours) and the lowest was for the Firth of Tay (9.62 ± 37.84 hours). The median trip duration for the other sites: the Baie du Mont Saint Michel, the Baie des

	Veys, the Baie de Somme and the Kenmare Bay were 11.43 ± 14.36 hours, 14.60 ±
	18.77 hours, 11.28 ± 29.97 hours and 17.45 hours ± 15.39 hours respectively. The
	intra-site variability was also highlighted with 22.86% to 86.66% of possible individual

.1-1). A post hoc Dunn test was used to do pair-sites comparison. Each site was significantly different from the others (p<0.05, Figure 5.1-1), excepting the pair: Firth of Tay / pair comparisons being significantly different. The Inner Hebrides was the site where individual seals performed most similar trip durations, with only 16 pairs significantly different among 70 (22.86 %). On the contrary, in the Baie du Mont Saint Michel 13 pairs were significantly different among 15 (86.66%

Table 5 .1-1: Summary of individual-pairs (in red, nb of significantly different pairs/total nb) and site-pairs (pvalue) of Dunn test for harbour seal trip durations in the Baie du Mont Saint Michel (BdM), Baie des Veys (BdV), Baie de Somme (BdS), Kenmare bay (Kenmare), Inner Hebrides (Inner H.), Firth of Tay (F.o.T)

 5

		BdM	BdV	BdS	Kenmare	Inner H.	F.o.T
	BdM	13/15	<0.05	<0.05	<0.05	<0.05	<0.05
	BdV		43/76	<0.05	<0.05	<0.05	<0.05
	BdS			39/64	<0.05	<0.05	0.47
	Kenmare				18/64	<0.05	<0.05
	Inner H.					16/70	<0.05
	F.o.T.						37/52

  the lowest percentage of individual pairs significantly different (27 pairs of 70 i.e. 38.57%), and the Baie du Mont Saint Michel had the highest percentage (11 individual pairs being significantly different of 15, i.e. 73.33%). The inter-site variability is shown (Figure 5.1.2-2). The range of values of trip maximum extent for seals tagged in the Inner Hebrides was larger than for those tagged in the other sites.

	Ten pair-sites over 15 were
	significantly different (i.e. representing 66.67%, Figure 5.1.1-2). The median of trip
	maximum extent was the highest in the Inner Hebrides (5.96 ± 14.71 Km) and the
	lowest in the Firth of Tay (3.62 ± 10.98 Km). The medians of trip maximum extents in

the Baie du Mont Saint Michel, Baie des Veys, Baie de Somme and Kenmare Bay were 5.90 ± 2.78 Km, 5.34 ± 12.31 Km, 4.85 ± 9.99 Km and 5.47± 6.75 Km respectively.

Inter-individual variability was also highlighted with 42.19% to 73.33% of possible individual-pairs comparisons being significantly different. The Inner Hebrides was the site with

Table 5 .1-2: Summary of individual-pairs comparison (in red, nb of significantly different pairs/total nb) and site-pairs (pvalue) comparison with Dunn test for harbour seals' trip maximum extent in the Baie du Mont Saint Michel (BdM); Baie des Veys (BdV); Baie de Somme (BdS); Kenmare Bay (Kenmare); Inner Hebrides (Inner H.) and Firth of

 5

	Tay (F.o.T)
		BdM	BdV	BdS	Kenmare	Inner H.	F.o.T
	BdM	11/15	<0.05	<0.05	<0.05	<0.05	<0.05
	BdV		49/76	0.05	<0.05	0.84	<0.05
	BdS			28/64	0.23	0.47	<0.05
	Kenmare				35/64	0.23	<0.05
	Inner H.					27/70	<0.05
	F.o.T.						36/52

Table 5 .2-1: Results of GAMM model for the foraging habitat selection of harbour seals in the Baie du Mont Saint Michel

 5

	Smoother term	edf	Chi-square	Pvalue
	Distance from last haulout	2.96	1,988	<0.0001
	Distance from shore	2.99	343.40	<0.0001
	Bathymetry	2.91	643.5	<0.0001
	Current	2.98	991.2	<0.0001
	Seal	3.42	113.8	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (coarse-sed.)	-26.65	-9.54	<0.0001
	Mixed-sediment	1.72	1.04	0.09
	Mud	-2.25	-2.05	0.04
	Rock	2.14	2.03	0.04
	Sand	0.56	0.54	0.59

Table 5

 5

.2-2). The deviance explained by this model was 70.3%. The bathymetry explained most of the deviance: 62.5%. The other variables: distance from the last haulout, tidal current, and sediment types explained 29.20, 2.54 and 4.20 % of the model deviance respectively (Figure 5.2-4, Table

Table 5 .2-2: Models of foraging habitat selection in the Baie des Veys; Deviance explained (DE); Use Availability Design (UAD); model selected is in bold

 5

	Model				AIC	Delta	DE
							AIC
	UAD selection ≈ s(dist_HL) + s(bathy)	39,718	0.00	70.3%
	+ factor(sediment) + s(tidal_current)
	+ s(ref, bs="re)
	UAD	selection	≈	s(dist_HL)	+	42,183	2465	68.5%
	s(dist_shore)	+ factor(sediment) +
	s(tidal_current) + s(ref, bs="re)

Table 5 .2-3: Results of the GAMM model for the foraging habitat selection of harbour seals in the Baie des Veys Smoother term edf Chi-square Pvalue

 5

	Distance from last haulout	2.84	9,647	<0.0001
	Bathymetry	2.95	3,027	<0.0001
	Current	2.99	702	<0.0001
	Seal	10.89	1,925	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (coarse-sed.)	-8.36	-9.004	<0.0001
	Mixed-sediment	-1.22	-12.68	<0.0001
	Mud	-1.46	-9.83	<0.0001
	Rock	0.13	1.45	0.15

Table 5 .2-4: Models of foraging habitat selection for harbour seals in the Baie de Somme; Deviance explained (DE); Use Availability Design (UAD); model selected is in bold

 5

	Model				AIC	Delta AIC	DE
	UAD selection ≈	s(dist_HL) +	43,592	0.00	77.3%
	s(dist_shore) + factor(sediment) +
	s(tidal_current) + s(ref, bs="re)
	UAD	selection	≈	s(bathy)	+	57,614	14,022	70.1%
	factor(sediment) + s(tidal_current) +
	s(ref, bs="re)

Table 5 .2-5: Results of the GAMM model for the foraging habitat selection of harbour seals in the Baie de Somme

 5

	Smoother term	edf	Chi-square	Pvalue
	Distance from last haulout	2.99	10,976	<0.0001
	Distance from shore	2.98	3,278	<0.0001
	Current	2.99	243	<0.0001
	Seal	8.87	2,254	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (coarse-sed.)	-65.45	-9.65	<0.0001
	Mixed-sediment	0.38	5.15	<0.0001
	Mud	-0.06	-0.30	0.77
	Rock	1.97	20.91	<0.0001
	Sand	0.30	3.95	<0.0001

Table 5 .2-6: Models of foraging habitat selection of harbour seals in the Kenmare Bay; Deviance explained (DE); Use Availability Design (UAD); the model selected is in bold

 5

	Model				AIC	Delta AIC	DE
	UAD selection ≈ s(dist_HL) + s(dist-	39,718	0.00	70.3%
	shore)	+ factor(sediment) + s(ref,
	bs="re)
	UAD	selection	≈	s(bathy)	42,183	2465	68.5%
	+factor(sediment) + s(ref, bs="re)

Table 5 .2-7: Results of the GAMM model for the foraging habitat selection of harbour seals in the Kenmare Bay

 5

	Smoother term	edf	Chi-square	Pvalue
	Distance from last haulout	2.99	29,500	<0.0001
	Distance from shore	2.98	1,341	<0.0001
	Seal	8.98	6,585	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (mixed-sed.)	-6.72	-22.47	<0.0001
	Mud	-1.93	-21.24	<0.0001

Table 5 .2-8: Results of the GAMM model of foraging habitat selection of harbour seals in the Inner Hebrides

 5

	Smoother term	edf	Chi-square	Pvalue
	Distance from last haulout	2.99	15,398	<0.0001
	Distance from shore	2.70	657	<0.0001
	Bathymetry	2.93	907	<0.0001
	Current	2.98	288	<0.0001
	Seal	8.99	3,908	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (coarse-sed.)	-24.09	-22.11	<0.0001
	Mixed-sediment	-2.14	-18.14	<0.0001
	Mud	-0.59	-9.85	<0.0001
	Rock	0.32	4.64	<0.0001
	Sand	-0.15	0.04	<0.001
	Shelf mound or pinnacle	0.13	0.70	0.50
	Shelf trough	-1.30	-9.52	<0.0001

Table 5 .2-9: Models of foraging habitat selection for harbour seals in the Firth of Tay; Deviance explained (DE); Use Availability Design (UAD); Model selected is in bold

 5

	Model					AIC	Delta AIC	DE
	UAD selection ≈ s(dist_HL) +	84,268	4,976	56.3%
	factor(sediment) + s(tidal_current) +
	s(ref, bs="re)
	UAD	selection	≈	s(dist_shore)	79,292	0.00	58.9%
	+s(bathy)	+	factor(sediment)	+
	s(tidal_current) + s(ref, bs="re)

Table 5 .2-10: Results of the GAMM model of foraging habitat selection of harbour seals in the Firth of Tay Smoother term edf Chi-square Pvalue

 5

	Distance from shore	2.99	9,162	<0.0001
	Bathymetry	2.96	16,333	<0.0001
	Current	3.00	6,608	<0.0001
	Seal	6.83	840	<0.0001
	Parametric coefficient	Estimate	Zvalue	Pvalue
	Intercept (sand)	-3.93	-39.98	<0.0001
	Bay	-1.39	-42.18	<0.0001
	Gravel-sand	-0.07	-2.09	0.04
	Rock	-0.32	-5.25	<0.0001

Table 3 .

 3 Parameter estimates of the variables determined to affect gray seal habitat selection in the Iroise Sea marine park, 2010-2013, and parameter confidence limits (2.5% and 97.5%) based on 95% confidence levels. Sediments coefficient values are relative to the reference of coarse sediments.

	Variable	b Coefficient	P-value	2.5%	97.5%
	Intercept	4.719	0.001	4.48	4.95
	Distance to haulout (m)	À1.374e-4	0.001	À1.43e-4	À1.32e-4
	Bathymetry (log) (m) Bathymetry 2 (m)	À0.053 3.760e-4	0.001 0.001	À0.059 3.24e-4	À0.046 4.28e-4
	Distance from shore (m)	À4.909e-5	0.001	À6.148e-5	À3.69e-5
	Current (m/s)	À0.641	0.001	À0.852	À0.426
	Sediments
	Mixed sediments	0.678	0.001	0.506	0.852
	Mud	À0.772	0.001	À1.13	À0.415
	Rock	0.229	0.005	0.070	0.387
	Sand	À0.033	0.702	À0.021	À0.426
	1096			The Journal of Wildlife Management 79(7)

Table 2 -

 2 Results from the application of First Passage Time (FPT) analysis (horizontal approach) on each harbour and grey seals' trips to assess the Areas of Restricted Search (ARS). The results are summarized for each individual.

						First Passage Time analysis
	Species	Individual Number of trips	Trip duration (h) mean ± SD	Number of trips with ARS	Spatial scale r of ARS (km) mean ± SD	Duration of trip with ARS (h) mean ± SD
		G01	125	19.0 ± 57.7	64	1.9 ± 5.0	32.2 ± 78.7
		G02	57	28.5 ± 75.7	12	8.2 ± 11.5	113.9 ± 138.3
	seals	G03 G04	180 17	12.1 ± 19.8 16.4 ± 20.4	67 8	1.2 ± 1.2 1.8 ± 2.1	28.5 ± 25.0 31.6 ± 21.1
	Grey	G05 G06	126 100	19.0 ± 28.9 18.9 ± 35.0	69 27	2.2 ± 2.8 5.7 ± 5.7	32.3 ± 33.6 60.8 ± 46.4
		G07	80	20.7 ± 78.0	23	2.7 ± 7.7	57.3 ± 140.9
		G08	95	20.1 ± 73.9	20	5.7 ± 11.4	72.2 ± 152.9
		S01	84	18.0 ± 19.9	48	0.6 ± 0.4	26.1 ± 21.4
		S02	177	21.3 ± 52.1	65	2.0 ± 3.2	48.2 ± 76.2
	seals	S03 S04	169 84	17.5 ± 16.9 22.5 ± 40.3	72 29	0.8 ± 0.9 0.6 ± 0.3	27.4 ± 19.6 33.7 ± 40.4
	Harbour	S05 S06 S07	109 87 229	14.1 ± 12.4 13.8 ± 17.7 14.2 ± 19.5	67 39 90	0.8 ± 0.6 0.8 ± 0.8 1.0 ± 0.9	19.1 ± 12.0 24.1 ± 21.1 28.6 ± 22.9
		S08	165	22.6 ± 29.4	76	1.7 ± 2.3	37.7 ± 34.6
		S10	107	20.8 ± 28.0	52	0.9 ± 1.2	34.5 ± 33.0

Table 1

 1 Number of seals tagged by species, sex, location and year, with deployment details (tag type and mean tracking duration) as well as reference when tracking data was previously published.

	Year of deployment Species	Tag type	Number of tags Sex ratio (M:F) Mean tag lifespan (days) Tagging locations	Refs.

Table 3

 3 Percentage of haulouts and percentage of at-sea (interpolated) locations of the tracked seals of both species located in Special Areas of Conservation (SACs) or other Marine Protected Areas (MPAs, such as marine national parks and offshore SACs) in different European countries.

	Percentages of tracking locations in	Harbour seals		Grey seals
	SACs and MPAs
			BSM	BDV	BDS	MOL	BDS
	Haulout events	France	100.0% 98.2% 100.0% 77.4% 45.3%
		UK	-	-	-	3.2%	9.1%
		Ireland	-	-	-	12.5% -
		Netherlands	-	-	-	-	11.8%
		TOTAL	100.0% 98.2% 100.0% 93.1% 66.2%
	At-sea	France	98.9%	74.1% 86.3%	72.4% 41.8%
	(interpolated)	UK	-	-	-	2.5%	3.6%
	locations	Ireland	-	-	-	2.0%	-
		Netherlands	-	-	-	-	5.8%
		TOTAL	98.9%	74.1% 86.3%	76.9% 51.2%

  3. First, we test the normality and homoscedasticity of trip characteristic distribution by using respectively, Shapiro and Bartlett test (from Stat R package with the function shapiro.test and Bartlett.test). If the normality and homoscedasticity were validated, we used an ANOVA to compare inter and intra sites plasticity, otherwise, we used a Kruskal Wallis test (both test were in the "Stat" R package with kruskal.test and aov functions). If the case of intra site plasticity was validated, we used a post hoc test to compare the difference per individual pairs. We used the tuckey HSD test (tuckey HSD function in Stat package), in the case of ANOVA; and dunn test (dunn.test function in the

"dunn.test" package) for Kruskal Wallis test..

Table 1 : Details of grey seals tagged with Fastloc GPS/GSM tag at Raven Point and Great Blasket Island. Only seals included in the analyses are presented in the table

 1

	Site	Seal	S	Mass	Nose	Start_tracking End_tracking Tracking	Period	Number	Number of	Number
			e	(Kg)	Flipper			duration	selection	of GPS	interpolate	dives
			x		length					locations	d locations
					(cm)
	Great	gp14-	F 121	170	25/02/09	22/07/09	147	22/07/09 3037	8213	26952
	Blasket	10957-
		09
	Great	gp14-	F 78	141	25/02/09	16/10/09	233	16/10/09 2858	10423	58281
	Blasket	11093-
		09
	Great	gp14-	F 68	141	25/02/09	02/10/09	219	02/10/09 2465	8084	56495
	Blasket	11101-
		09
	Great	gp14-	F 119	159	25/02/09	12/05/09	76	12/05/09 602	1735	9856
	Blasket	11108-
		09
	Great	gp14-	F 70	138	25/02/09	11/07/09	136	11/07/09 2650	7943	35152
	Blasket	11113-
		09
	Great	gp14-	F 90	152	25/02/09	13/11/09	261	15/11/12 5293	15686	53859
	Blasket	11015-
		09
	Great	gp14-	F 110	159	25/02/09	06/10/09	223	06/10/09 3255	11797	43919
	Blasket	11095-
		09
	Great	gp14-	F 115	166	25/02/09	09/12/09	287	31/10/12 5976	17479	44843
	Blasket	11100-
		09
	Great	gp16-	M 162	171	15/03/11	28/05/11	74	28/05/11 2703	3785	16638
	Blasket	x377-11

Table 2 : Individual value of foraging criteria selection: Time At Depth (Depth), Vertical Speed (S), Descent Speed (V1); (*) average value RESUME

 2 La plasticité comportementale et les interactions entre organismes sont connues pour avoir une influence sur l'utilisation de l'espace, notamment la sélection de l'habitat de chasse. Dans l'Atlantique Nord-Est, les phoques gris et veaux marins vivent en sympatrie. Ces deux espèces sont réparties dans différentes colonies où les interactions entre organismes (liées à la taille de la colonie et à la présence simultanée ou non des deux espèces) ainsi que la disponibilité de l'habitat (et donc des proies) varient localement. L'objectif de cette thèse était d'étudier l'utilisation spatiale et la sélection d'habitat de chasse à l'échelle locale de ces deux espèces dans des situations contrastées de dynamique de colonies. Pour cela, plusieurs individus de différentes colonies situées en Irlande, en Ecosse et en France ont été équipés de balises GPS/GSM. Les données récoltées ont permis de caractériser leurs trajets en mer ainsi que d'identifier les zones de chasse permettant par la suite la sélection d'habitat. Contrairement aux veaux marins, qui ont réalisé des déplacements restreints aux alentours des colonies, la plupart des phoques gris se sont déplacés entre différentes colonies. Ainsi, l'étude de la sélection d'habitat à l'échelle individuelle pour les individus se déplaçant entre différentes colonies a mis en évidence l'influence de la plasticité comportementale et suggéré celle de la personnalité de l'animal. A l'échelle de la colonie, chez les deux espèces, les résultats ont mis en évidence l'influence de la disponibilité de l'habitat (et indirectement de la disponibilité des proies) ainsi que des interactions intra-spécifiques (liée à l'effet de densité dépendance) sur l'utilisation spatiale et la sélection de l'habitat de chasse. Cependant, la sélection de l'habitat de chasse était principalement lié à la distance au reposoir et la bathymétrie. Ces deux paramètres sont liés aux caractères de chasseur à place centrale et benthique des phoques. Cette étude a également mis en évidence, pour les sites où les deux espèces étaient présentes, une certaine ségrégation spatiale et des différences de sélection d'habitat entre les deux espèces.

Mots clé : Phoque gris ; Halichoerus grypus ; Phoque veau marin ; Phoca vitulina ; Espèces sympatriques ; Utilisation d'habitat ; Plasticité comportementale ; Interactions intra et inter-specifiques.

† http://www.emodnet-seabedhabitats.eu/default.aspx

The Journal of Wildlife Management 79(7)

http://www.smru.st-andrews.ac.uk/Instrumentation/downloads/GPS_Phone_Tag22.pdf

Wildtrack Telemetry Systems Limited (https://wildlifecomputers.com/data-products/fastloc/)

http://www.smru.st-and.ac.uk/Instrumentation/Downloads/ (SRDL overview)

http://www.smru.st-and.ac.uk/Instrumentation/GPSPhoneTag/

Acknowledgements

ACKNOWLEDGMENTS

We wish to thank F. Lecornu from the Institut FranS cais pour la Recherche et l'Exploitation de la MER and L. Boehme for their help in environmental variable database management; the Matthiopoulos group, C. Blight and P. Lovell for their programming support. We thank S. Moss, C. Hue, the Parc naturel marin d'Iroise, the Office National de la Chasse et de la Faune Sauvage (ONCFS), the Obervatoire PELAGIS, and Oc eanopolis for their help in the field. The article was improved by the helpful reviews of E. Merrill, J. Sheppard, and 2 anonymous reviewers. The project was funded by the Parc naturel marin d'Iroise and the Regional Council of Poitou-Charentes (France). M. Huon's work in SMRU was partly funded by Erasmus. E.L.J. was funded under Scottish Government grant MMSS001/11.

Acknowledgements

This paper combines data obtained over a long period of time, by a large number of people belonging to varying institutions. We wish to sincerely thank all people who agreed to share their data. Counts on haulout sites at BDS, BDA and WAL were done by many volunteers who we greatly thank. Seal work at BDS is part of the management plan for the Réserve Naturelle de la Baie de Somme and is funded by the DREAL Picardie, Conseil Général de Picardie and Conseil Départemental de la Somme. In BSM, aerial surveys of the haulout sites are conducted by Sébastien Cottin and Frédéric Fortin, and funded by DREAL Basse Normandie. In SEP, the authors wish to thank Gilles Bentz, Régis Perdriat, Vincent Bretille, Frank Herrmann and François Siorat for their contribution to the seal counts. At WAL, we thank Clémentine Brévart for the preparation of the database. At BDS, BDA and WAL, since 2015 seal work is funded by Région Nord-Pas de Calais through the Eco-Phoques project. We also thank the Office national de la chasse et de la faune sauvage (ONCFS), Agence des Aires Marines Protégées and Bretagne Vivante who provided information on minor seal haulout sites along the French coast (Yannick Jaouen, Yann Jacob, Benjamin Pellegrini, Cécile Gicquel; cf. Fig. 1). Telemetry work was funded by DREAL Bretagne, FEOGA Funds (EU), DREAL Basse Normandie, Région Poitou-Charente, La Compagnie du Vent, and the Parc naturel marin d'Iroise. Field work

I would like to thank all people who agreed to share their data and the institutions who provided the fundings. In France, telemetry work was funded by DREAL Bretagne, FEOGA Funds (EU), DREAL Basse Normandie, Région Poitou-Charente, La Compagnie du Vent, and the Parc naturel marin d'Iroise. Field work involved many participants belonging to the Sea Mammal Research Unit, University of La Rochelle (LIENSs, PELAGIS, CEBC),

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SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article at the publisher's website. 1100 The Journal of Wildlife Management 79(7)

Horizontal and vertical approaches highlight consistent foraging areas for harbour and grey seals.

Seals handling and tagging

Nine harbour seals and eight grey seals were captured in the Baie de Somme on October 2008 and May 2012 respectively, and were fitted with Fastloc TM GPS/GSM tags developed by Sea Mammal Research Unit (University of St-Andrews, UK).

Based on previous GPS phone tags (McConnell et al., 2004), these tags include a Fastloc TM GPS, a GSM modem, wet-dry, pressure and temperature sensors, and a microprocessor 1 . GPS locations, dives and haulout data were provided by the tags. A GPS location was expected every 20 minutes when the seal was at sea, however this frequency could be lower when the seal spent much time underwater. Consequently GPS locations data sets are irregular in time. Ninety nine percent of the Fastloc GPS locations lie within 50m of the true location 2 . Following McConnell et al. (1992), these locations were filtered in order to remove those which appeared unrealistic (horizontal swim speed > 3.2 m.s -1 , in order to take into account strong tidal currents in the area). The wet-dry and pressure sensors determine 3 states: hauling out, swimming at the surface and diving (below a depth threshold of -1.5 m). Dive data transmitted by the tag include:

start date/time, maximum depth, dive duration, post-dive surface duration, the "percent area" (allowing the calculation of Time At Depth index, TAD; Fedak et al., 2001) and 9

intermediate depth points (depth information are summarized by the tag in order to yield intermediate depths each 10% of the dive duration; Fedak et al., 2001). Each dive location was interpolated between the previous and next at-sea Fastloc GPS locations.

Use of telemetric data

We defined a trip as the whole horizontal movement at sea (i.e. GPS locations at sea) which occurred between two consecutive haulout events. Only trips which entirely occurred on the study area and during the study period were included in the analyses. In order to identify foraging areas, all selected trips were used for the First Passage Time analysis (Fauchald and Tveraa, 2003) which is the horizontal approach chosen in this study. We then selected dives occurring during the same trips in order to perform a criteria-based analysis as a vertical approach. By working on the same individual trips, we aimed at performing a rigorous comparison between the horizontal and vertical approaches for the assessment of foraging areas.

Abstract

Central place foragers exploit resources within a range of a 'central place' (e.g. breeding sites for seabirds, haulout sites for pinnipeds). The spatial distribution of resources and, consequently the effect of density dependence, will have an impact on foraging habitat selection. Furthermore, foraging habitat selection is also driven by behavioural plasticity and environmental variability. The objectives of this study were to (i) test the effect of density-dependence on the foraging behaviour of two grey seal colonies of different sizes, from the eastern and western seaboards of Ireland and (ii) examine grey seal habitat selection on the continental shelf and in the Irish Sea. Grey seals generally selected foraging habitat close to their haulout sites, and consequently shallow waters.

However, seals tagged in the Irish Sea (eastern Irish seaboard) made longer trips (in duration and extent) compared to those tagged in the Atlantic (western Irish seaboard).

Mixed models were used to examine foraging habitat selection, with models suggesting that distance from the last haulout site and bathymetry explained the majority of the model deviance at both sites. The foraging habitat selection differed between the two sites with respect to distance from shore, sediment type, and tidal current. On the continental shelf, 'distance from shore' had a bimodal influence on grey seal habitat selection, seals selected foraging habitat both close to and at a distance greater than 200km from the shore. Seals selected rocky seabed, avoiding mud and sand, and preferred areas of highest tidal current. In the Irish Sea, distance from shore had a weak influence on foraging habitat selection; grey seals selected muddy and sandy habitat and also selected areas of highest tidal current. Finally, the expected effect of density dependence on greys seal foraging behaviour and habitat selection was not apparent.

Foraging ranges for grey seals on the continental shelf and in the Irish Sea instead appear to be shaped by resource availability and possibly interactions with fisheries.

Therefore foraging habitat selection of grey seals in Irish waters is driven by both biological and physical processes.

ABSTRACT

The behavioural plasticity and the interactions between organisms are known to influence foraging habitat selection and spatial usage. In the Northeast Atlantic, grey and harbour seals live in sympatry. These two species are spread into different colonies where interactions between organisms (linked to the colony size et and the presence of one or both species) and habitat availability (linked to the prey distribution) vary locally. The objective of this PhD was to study the spatial usage and foraging habitat selection of these two species at the local scale, in contrasted situations of population dynamics. Several individuals were tagged with GPS/GSM tags in different colonies located in Ireland, Scotland and France. Telemetry data was used to characterize the seals' trips at sea and to identify their foraging areas in order to model foraging habitat selection. While harbour seals only performed trips restricted around their haulout sites, grey seals moved between colonies. Studying habitat selection at the individual scale, for individuals moving between colonies, highlighted the influence of behavioural plasticity and suggested the influence of individual personality. At the colony scale, for both species, results highlighted the influence of habitat availability (indirectly linked to prey availability) and intra-specific interactions (linked to density dependence effects) on spatial usage and foraging habitat selection. However, the foraging habitat selection was mainly explained by the distance from the last haulout and the bathymetry. These two parameters are linked to the central place forager and benthic feeder characteristics of the seals. Furthermore, this study also highlighted, for study sites where both species occur, a spatial segregation and differences in foraging habitat selection between grey and harbour seals.