Jeremy Kiszka
email: jeremykiszka@wanadoo.fr

Benoit Simon-Bouhet

Ludivine Martinez

Claire Pusineri

Pierre Richard

Vincent Ridoux

Ecological niche segregation within a community of sympatric dolphins around a tropical island

Keywords: tropical dolphins, ecological niche, habitats, stable isotopes, carbon, nitrogen, south-west Indian Ocean, Mayotte

Investigating ecological segregation among organisms of a given community is challenging, especially when these organisms share similar patterns of distribution, and similar size and morphology. Around the island of Mayotte, a diversified community of at least four sympatric delphinids is present year round within a very restricted range: the Indo-Pacific bottlenose dolphin (Tursiops aduncus), the spinner dolphin (Stenella longirostris), the pantropical spotted dolphin (Stenella attenuata), and the melon-headed whale (Peponocephala electra). In addition, the Fraser's dolphin (Lagenodelphis hosei) makes temporary incursions into peri-insular waters as well. This study aims to assess niche segregation among this tropical dolphin community. We hypothesized that each species occupies its own distinct niche, defined by the following axes: habitat, resources and time. We analysed habitat in relation to physiography, behavioural budgets and C and N stable isotope values from skin and blubber samples for each species. The results highlighted that habitat and behavioural budgets were relatively distinct among species, with few exceptions. However, in those species living on the outer reef slope where habitat and behaviour were not well discriminated, stable isotope analyses confirmed that species have different trophic levels (mostly reflected through δ 15 N values) and/or foraging habitat (mostly reflected through δ 13 C values). This study confirms that the use of multiple methodologies (habitat, behaviour and feeding ecology studies) help in discerning ecological niche segregation, especially when examining closely related species within a common restricted range.

INTRODUCTION

Understanding niche segregation processes is critical in ecology, particularly when investigating the ecology of species communities. A community can be defined as a collection of species that occur together in a common environment, or habitat, the organisms making up the community being somehow integrated or interacting as a society [START_REF] Chapman | Ecology -Principles and Applications[END_REF]. Each species has its own unique niche [START_REF] Grinnell | Geography and evolution[END_REF]. The ecological niche is a complex set of variables structured along three main axes: habitat (influence of environmental variables), diet (diet composition, trophic level and prey quality) and time (use of habitat and resources according to time, such as seasons and time of day). Sympatric species with similar ecological requirements would compete for resources and their coexistence requires some degree of habitat and resource segregation [START_REF] Pianka | Niche overlap and diffuse competition[END_REF]. Similar species that co-occur are thought to compete for resources unless they occupy different physical locations and/or feed on different prey. A shared resource in limited supply will bring about competition between members of the same species (intraspecific competition) or between individuals of different species (inter-specific competition) [START_REF] Roughgarden | Resource partitioning among competing species: a coevolutionary approach[END_REF]. Intra-specific competition may be expressed by sex or age related difference in habitat and resource use and has consequences on social structures. Inter-species competition can take various forms, including direct interference (aggressive behaviour) and exploitation-competition, in which individuals indirectly compete for resources [START_REF] Begon | Ecology -Individuals, Populations and Communities[END_REF]).

Investigating segregation processes within communities of organisms having similar size and morphology has been particularly challenging. In such communities, niche partitioning is difficult to assess as it can occur over small spatial and temporal scales. For example, in species with similar morphology (e.g. body size, jaw/beak shape, etc.), feeding niches are distinct even when feeding occurs in both species within the same area [START_REF] Macarthur | Population ecology of some warblers of northeastern coniferous forests[END_REF]. Niche segregation has been investigated in a number of top marine predator communities, including large teleost fish [START_REF] Potier | Feeding partitioning among tuna taken in surface and mid-water layers: the case of yellow fin (Thunnus albacares) and bigeye (T. obesus) in the western tropical Indian Ocean[END_REF][START_REF] Ménard | Isotopic evidence of distinct feeding ecologies and movement patterns in two migratory predators (yellowfin tuna and swordfish) in the western Indian Ocean[END_REF], sharks [START_REF] Estrada | Predicting trophic position of sharks in the north-west Atlantic Ocean using stable isotope analysis[END_REF][START_REF] Domi | Feeding ecology of five commercial shark species of the Celtic Sea through stable isotope and trace metal analysis[END_REF][START_REF] Papastamatiou | Distribution and diet of four species of carcharhinid shark in the Hawaiian Islands: evidence for resource partitioning and competitive exclusion[END_REF], seabirds [START_REF] Ridoux | The diets and dietary segregation of seabirds at the subantarctic Crozet Islands[END_REF][START_REF] Cherel | Resource partitioning within a tropical seabird community: new information from stable isotopes[END_REF][START_REF] Jaeger | Etude isotopique des variations saisonnières et à long terme de l'écologie alimentaire des oiseaux marins de l'océan austral[END_REF], marine mammals [START_REF] Das | Marine mammals from the southern North Sea: feeding ecology data from δ13C and δ15N measurements[END_REF][START_REF] Whitehead | Differences in niche breadth among some teuthivorous mesopelagic marine mammals[END_REF][START_REF] Praca | Ecological niches of three teuthophageous odontocetes in the northwestern Mediterranean Sea[END_REF] including delphinids [START_REF] Pusineri | Feeding niche segregation among the northeast Atlantic community of oceanic top predators[END_REF][START_REF] Gross | A preliminary study of habitat and resource partitioning among co-occurring tropical dolphins around Mayotte, southwest Indian Ocean[END_REF][START_REF] Kiszka | The use of stable isotope analyses from skin biopsy sampled to assess trophic relationships of sympatric delphinids off Moorea (French Polynesia)[END_REF]. Methods used to discriminate niches were variable, including stomach content, stable isotope and heavy metal analyses and habitat assessment (including habitat modelling). For example, niche partitioning has been assessed in polar communities using stable isotope analyses of C and N, such as in Antarctic pinnipeds, showing clear ecological segregation between species [START_REF] Zhao | Trophic interactions of Antarctic seals as determined by stable isotope signatures[END_REF]). Conversely, in tropical sympatric seabirds, important overlap of feeding niches has been found, which may be interpreted by the low productivity of tropical oligotrophic waters, leading these predators to share same feeding resources that are not quantitatively limited [START_REF] Cherel | Resource partitioning within a tropical seabird community: new information from stable isotopes[END_REF]). In the tropical cetacean community of the Bahamas, it has been shown using a habitat analysis that the ecological niches of four cetacean species (Atlantic spotted dolphin Stenella frontalis, Blainville's beaked whale Mesoplodon densirostris, Cuvier's beaked whale Ziphius cavirostris and dwarf sperm whale Kogia simus) do not overlap. Other cetacean species are observed in the area only during the season when prey abundance is sufficiently high to support their presence, while they are competitively excluded for the rest of the year [START_REF] Macleod | Diversity, relative density and structure of the cetacean community in summer months east of Great Abaco, Bahamas[END_REF]).

Around the tropical island of Mayotte (Comoros, SW Indian Ocean), a great diversity of cetaceans has been found within a limited geographical range, i.e. at least 19 species within an area of 2,500 km² [START_REF] Kiszka | Cetacean diversity around the Mozambique Channel island of Mayotte (Comoros Archipelago)[END_REF]. In this area, high cetacean diversity may be associated by the presence of a wide range of marine habitats within close proximity to one another: turbid mangrove fronts, fringing reef systems, clear lagoon areas, barrier and double barrier reef-associated habitats, a steep insular slope and deep oceanic waters. In some locations around Mayotte lagoon, water depth extends beyond 1,000 m less than 3 km away from the barrier reef.

The permanent presence of odontocetes within a restricted range suggests that fine-scale mechanisms allow for the partitioning of habitats and/or resources. The four most common dolphin species there have a size ranging from 1.8 m spinner dolphin (Stenella longirostris, the smallest) to the 2.8 m melon-headed whale (Peponocephala electra, the largest). Other species include Indo-Pacific bottlenose dolphin (Tursiops aduncus), pantropical spotted dolphin (Stenella attenuata) and Fraser's dolphin (Lagenodelphis hosei) [START_REF] Kiszka | Cetacean diversity around the Mozambique Channel island of Mayotte (Comoros Archipelago)[END_REF]). The Indo-Pacific bottlenose dolphin is typically a coastal species, feeding on inshore prey [START_REF] Amir | Feeding ecology of the Indo-Pacific bottlenose dolphin (Tursiops aduncus) incidentally caught in the gillnet fisheries off Zanzibar, Tanzania[END_REF] and lives inside the lagoon around Mayotte [START_REF] Gross | A preliminary study of habitat and resource partitioning among co-occurring tropical dolphins around Mayotte, southwest Indian Ocean[END_REF]. Conversely, the other species of the community are oceanic and primarily occur outside the lagoon and feed on epipelagic to mesopelagic oceanic prey [START_REF] Dolar | Comparative feeding ecology of spinner dolphins (Stenella longirostris) and Fraser's dolphins (Lagenodelphis hosei) in the Sulu Sea[END_REF][START_REF] Brownell | Behavior of melon-headed whales, Peponocephala electra, near oceanic islands[END_REF]. A preliminary study of the tropical delphinid community around the island of Mayotte indicated that their ecological niches at least partially, overlapped [START_REF] Gross | A preliminary study of habitat and resource partitioning among co-occurring tropical dolphins around Mayotte, southwest Indian Ocean[END_REF]. Using sighting data related to environmental variables and stable isotope analyses from biopsy samples, it was shown that Indo-Pacific bottlenose dolphin had a coastal/lagonal distribution, while spinner dolphin, melon-headed whale and pantropical spotted dolphin had similar habitat characteristics along the outer reef slope.

Stable isotope analyses from a small amount of biopsy samples allowed species of the community to be discriminated isotopically, except the two congeneric and "sibling" dolphins of the genus Stenella, having similar morphological characteristics and frequently forming interspecies aggregations. Methodological constraints and limited sample size are likely to explain the absence of measurable differences between species. Therefore, the present work aims to characterize habitat and resource partitioning among delphinids living in sympatry around the island of Mayotte from multiple lines of evidence, along the following axes: habitat, diet (more particularly trophic level and foraging habitat) and time (seasons and time of day, Table 1). In this study, we hypothesize that each delphinids species occupies its own ecological niche, defined by at least one of the indicator used. Indeed, for any given pair of species, statistical difference should be found for at least one of the variables tested (habitat, stable isotope values and temporal variations). We will investigate habitat of delphinids in relation to physiographical variables, activity budgets and their variability among species and according to time, and stable isotope analyses (δ 13 C and δ 15 N) from biopsy samples. δ 13 C and δ 15 N isotopes help elucidate habitat use (e.g. δ 13 C values typically vary from 13 C depleted in offshore, or pelagic-derived, to 13 C enriched from inshore or benthic-derived C) and the position of the consumer in the food chain, respectively [START_REF] Hobson | Tracing origins and migration of wildlife using stable isotopes: a review[END_REF]. They can also reflect local baseline differences in coastal waters (Mallena & Harrod 2008). The use of these isotopes has provided alternative information from which to better understand top predator ecology, including marine mammals [START_REF] Das | Marine mammals from the southern North Sea: feeding ecology data from δ13C and δ15N measurements[END_REF].

Activity budgets have been investigated for the three most common species (spinner, spotted and Indo-Pacific bottlenose dolphins) in order to compare daily variation in behaviour and habitat utilization. It allows investigating, at a short time scale (time of day), one of the temporal dimension of the niche. We also assessed seasonal variations of habitat preferences, behavioural budgets and stable isotope signatures, as seasonality may be a major factor segregating species among them.

MATERIALS AND METHODS

Study area

Mayotte (45 o 10'E, 12 o 50'S) is located in the north-eastern Mozambique Channel, and is part of the Comoros archipelago (Figure 1). The island is surrounded by a 197 km long barrier reef, with a second double-barrier in the southwest and the immerged reef complex of Iris in the northwest.

The lagoon and surrounding reef complexes are 1,500 km² with an average depth of 20 m and a maximum depth of 80 m found in the western, older, region of the lagoon [START_REF] Quod | The Comoros archipelago[END_REF]. The insular slope on the exterior of the barrier reef is very steep and contains many submarine canyons and volcanoes [START_REF] Audru | Bathymay : la structure sous-marine de Mayotte révélée par l'imagerie multifaisceaux[END_REF]. The island of Mayotte is characterized by the presence of high cetacean diversity (19 species including 12 delphinids; [START_REF] Kiszka | Cetacean diversity around the Mozambique Channel island of Mayotte (Comoros Archipelago)[END_REF]). The most common species are the spinner dolphin (Stenella longirostris), the pantropical spotted dolphin (Stenella attenuata), the Indo-Pacific bottlenose dolphin (Tursiops aduncus) and the melon-headed whale (Peponocephala electra); these occur on a year-round basis [START_REF] Kiszka | Cetacean diversity around the Mozambique Channel island of Mayotte (Comoros Archipelago)[END_REF].

Data and sample collection

From July 2004 to April 2009, small boat based surveys were undertaken around Mayotte.

Several types of boats were used to collect data: a 7-m catamaran equipped with two, four-stroke, 60-hp outboard engines; a 7-m boat equipped with two, two-stroke, 40-hp outboard engines; a 6.4-m cabin cruiser equipped with one, four-stroke, 150-hp outboard engine; and a 10.8-m cabin cruiser equipped with two, four-stroke, 115-hp outboard engines. Surveys were conducted throughout the study period during daylight hours between 07:00 h and 18:00 h in sea conditions not exceeding Beaufort 3. Survey vessels did not follow pre-defined transects but every attempt was made to sample the whole daylight period as well as each habitat type within the surrounding waters of Mayotte, i.e. coastal areas, lagonal waters, barrier reef associated areas (inner and outer slopes) and oceanic or slope waters (>500 m). When delphinids were encountered, standard sighting data were recorded: species, group size (maximum, minimum, best estimate), geographic position and behavioural activity. The predominant activity was defined as the behavioural state in which most animals (> 50%) of the group were involved at each instantaneous sampling.

Typically, more than 90% of the animals in a group were engaged in the same activity.

In order to measure behaviour of the focal dolphin species and determine their behavioural budgets, focal group follows were used [START_REF] Mann | Behavioral sampling methods for cetaceans: a review and critique[END_REF]. While one of the preferred option in behaviour sampling is to follow a focal individual [START_REF] Mann | Behavioral sampling methods for cetaceans: a review and critique[END_REF], this method was not suitable for large aggregations of oceanic dolphins. In addition, following groups, rather than individuals, is more suitable for behavioural studies as appropriate conditions for individual sampling are rare in diving cetaceans [START_REF] Whitehead | The group strikes back: follow protocols for behavioral research on cetaceans[END_REF]). Individual follows were generally possible in the easily identifiable Indo-Pacific bottlenose dolphins, but not on each occasion, as some individuals were not identifiable, but for comparative purposes, we used a focal group protocol, which has been used in other studies on similar models [START_REF] Neumann | Impacts of ecotourism on short-beaked common dolphins (Delphinus delphis) in Mercury Bay, New Zealand[END_REF]. The encountered group was approached slowly (typically at 2-3 knots), from the side and rear, with the vessel moving in the same direction as the animals. Groups were scanned, including all individuals, to negate attention being drawn to only specific individuals or behaviours [START_REF] Mann | Behavioral sampling methods for cetaceans: a review and critique[END_REF]. During focal follows, dolphin behaviour was recorded every 5 minutes. Five categories of behavioural states were defined: milling, resting, travelling, feeding/foraging (hereafter foraging), and socializing as defined in previous studies (e.g. [START_REF] Norris | Behavior of the Hawaiian spinner dolphin, Stenella longirostris[END_REF][START_REF] Bearzi | Dolphin sympatric ecology[END_REF][START_REF] Neumann | Impacts of ecotourism on short-beaked common dolphins (Delphinus delphis) in Mercury Bay, New Zealand[END_REF][START_REF] Degrati | Diurnal behaviour of dusky dolphins, Lagenorhynchus obscurus, in Golfo Nuevo, Argentina[END_REF]. Feeding was characterised by loose to disperse group formations and dolphins were observed swimming in circles, and pursuing fishes (prey observed at the surface).

Preys were frequently seen at the surface during foraging activity. In bottlenose dolphins, large preys were frequently exhibited by the animals at the surface. Travelling consisted of persistent and directional movements of all the individuals of a group. Milling was characterised by nondirectional movements of the dolphin, with frequent changes in heading. Socialising consisted in frequent interactions between individuals in the form of body contacts, with high-speed movements, frequent changes in direction and aerial displays. Resting was characterised by low level of activity, with groups in tight formations, with little evidence of forward propulsion.

Surfacings are slow and relatively predictable.

For stable isotope analyses, biopsy attempts were made when groups and individuals were easily approachable and when conditions were optimal (Beaufort < 2, dolphins closely approaching the boat). Optimal weather conditions allowed stability of the research boat and better chances to sample the animals successfully and safely. Biopsies were collected by using a crossbow (BARNETT Veloci-Speed® Class, 68-kg draw weight) with Finn Larsen (Ceta-Dart, Copenhagen, Denmark) bolts and tips (dart 25-mm long, 5-mm diameter). A conical plastic stopper caused the bolt to rebound after the impact with the dolphin. The dolphins were hit below the dorsal fin when sufficiently close (3-10 m) to the research boat. Approaches of focal groups/individuals were made under power at speeds of 1-4 knots. Blubber and skin biopsy samples were preserved individually in 90% ethanol before shipping and subsequent analysis.

The preservative used (ethanol) was the most suitable that could be used due to logistical constraints. It does not affect stable isotope signatures in freshwater zooplankton and benthic macroinvertebrates [START_REF] Syväranta | Evaluating the utility of stable isotope analyses in archived freshwater sample materials[END_REF], bird eggs, blood and muscle [START_REF] Hobson | Preservation of blood and tissue samples for stablecarbon and stable-nitrogen isotope analysis[END_REF][START_REF] Gloutney | Field preservation techniques for the analysis of stable-carbon and nitrogen isotopes in eggs[END_REF]. The increase in δ 13 C values is generally considered to be due to the extraction of some lipids but because lipids are depleted in 13 C, they are typically extracted (or corrected arithmetically, e.g. [START_REF] Kiljunen | A revised model for lipid-normalising δ 13 C values from aquatic organisms, with implications for isotope mixing models[END_REF] to avoid a bias in estimates of δ 13 C values (De [START_REF] Niro | Influence of diet on the distribution of carbon isotopes in animals[END_REF][START_REF] Tieszen | Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ 13 C analysis of diet[END_REF], that likely cancels any potential effect of storage in ethanol [START_REF] Kiszka | The use of stable isotope analyses from skin biopsy sampled to assess trophic relationships of sympatric delphinids off Moorea (French Polynesia)[END_REF]. Biopsy sampling was conducted under French scientific permit #78/DAF/2004 (September 10, 2004) and permit #032/DAF/SEF/2008 (May 16, 2008) after examination of the project by Conseil National de Protection de la Nature.

Muscle samples from several fish species were also collected for stable isotope analyses, especially to investigate trophic interactions between delphinids and potential dolphin preys as well as fish species with clear ecological profiles (see below). Fish specimens were collected in a local fish market. Fish muscle samples were sampled in April 2009 and preserved in ethanol before subsequent analyses. The fish species selected were pelagic, demersal and benthic species from reef associated habitats, i.e. from various environments in the lagoon and surrounding waters, and different trophic levels (herbivorous, planctonophageous and piscivorous):

Hemiramphus far, epipelagic, inhabiting waters near reef systems and feeding on the pelagic zooplankton; Mulloidichthys vanicolensis, demersal on seaward reefs, feeding on small worms and crustaceans; Siganus argenteus, demersal, inhabiting coastal and inner reef slopes and feeding on algae; Scarus russelii, demersal, inhabiting shallow coastal reef and feeding on algae by grazing on coral bubble and Caranx melampygus, demersal and pelagic predator feeding on small schooling fishes [START_REF] Froese | FishBase[END_REF]. Two species were sampled because they regularly enter the diet of the Indo-Pacific bottlenose dolphin: Hemiramphus far and Caranx melampygus (J. Kiszka & C. Pusineri, personal observations).

Habitat analyses

We constituted a database in which every dolphin group observation was associated with the physiographic characteristics (distance from the coast, distance from the nearest reefs, depth and slope of seafloor) corresponding to the GPS (Global Positioning System) fixes of the observation. Bathymetric data were obtained from Service Hydrographique et Océanographique de la Marine (SHOM). Interpolation of bathymetry data, needed to generate depth and slope data for each sighting, was undertaken with the extension Spatial Analyst by kriging transformation of the raster file into an interpolated data file. The distance data were obtained using GIS (Geographic Information System) software ArcView (ArcGIS 8.3) by ESRI (Environmental Systems Research Institute). We represented the distribution of the four dolphin species investigated in relation to the environmental predictors using kernel density plots to view the distribution of species. In order to differentiate species niches, we performed Multidimensional Scaling (MDS), using Euclidian distances between individual habitat characteristics. Metric Multidimensional scaling (MDS) takes a set of dissimilarities and returns a set of points such that the distances between the points are approximately equal to the dissimilarities. It displays the structure of distance-like data as a geometrical picture [START_REF] Gower | Some distance properties of latent root and vector methods used in multivariate analysis[END_REF]. In other words, the purpose of MDS is to provide a visual representation of the pattern of proximities (i.e. similarities or distances) among a set of objects. This multivariate analysis was used in order to discriminate species in their habitat preferences. Presence-absence models were not used due to heterogeneous sampling of the study area (for further details, see [START_REF] Clarke | Change in marine communities: an approach to statistical analysis and interpretation[END_REF].

In order to complement this multivariate approach, univariate non-parametric pairwise Wilcoxon tests were used to compare species distribution for each environmental variables. Seasonal differences of habitat preferences were investigated using Mann-Whitney U-tests for each species in relation to the four environmental co-variates that were used. Two seasons were considered: rainy/summer (November -April) and dry/winter seasons (May -October). Analyses were performed using Rv2.10.0 (R Development Core Team, 2009).

Behavioural budget analyses

To analyse diel patterns of behaviour, we defined three time-blocks: morning (before 10:00 h), noon (between 10:01 h and 14:00 h) and evening (after 14:01 h). The seasons considered were identical to those used for the habitat analyses (dry/rainy season). Diurnal and seasonal patterns were investigated by assigning a behavioural sequence to a time block or a season. Contingency table analyses were used in order to compare behavioural budgets among species. Nonparametric tests were selected because assumptions regarding normality and homogeneity of variance were not met.

Stable isotope analyses

Blubber and skin were separated for each dolphin biopsy sample. Fish muscle tissues were used for stable isotope analyses. The ethanol was evaporated at 45°C over 48 h and the samples were ground and freeze-dried [START_REF] Hobson | Preservation of blood and tissue samples for stablecarbon and stable-nitrogen isotope analysis[END_REF]. Lipids were removed from both blubber and skin samples by 2 successive extractions (1 h shaking in cyclohexane at room temperature and subsequent centrifugation) prior to analysis. After drying, small sub-samples (0.35 to 0.45 mg + 0.001 mg) were prepared for analysis. Stable isotope measurements were performed with a continuous-flow isotope-ratio mass spectrometer (Delta V Advantage, Thermo Scientific, Germany) coupled to an elemental analyser (Flash EA1112 Thermo Scientific, Italy). Reference gas were calibrated against International Reference Materials (IAEA-N1, IAEA-N2 and IAEA-N3 for nitrogen; NBS-21, USGS-24 and IAEA-C6 for carbon). Results are expressed in the δ notation relative to PeeDee Belemnite and atmospheric N 2 for δ 13 C and δ 15 N, respectively, according to the equation:

1000 1 tan ×       - = dard s sample R R X δ
Where X is 13 C or 15 N and R is the isotope ratio 13 C/ 12 C or 15 N/ 14 N, respectively. Replicate measurements of a laboratory standard (acetanilide) indicated that analytical errors were <0.1‰ for δ 13 C and δ 15 N. Percent C and N elemental composition of tissues were obtained using the elemental analyzer and used to calculate the sample C:N ratio, indicating good lipid removal efficiency when C:N <4. Differences of stable isotopes values of δ 15 N and δ 13 C among species were tested using non-parametric Kruskal-Wallis and Mann Whitney U-tests. Seasonal variations were investigated using Mann-Whitney U tests. Like for seasonal variations of habitat preferences, the two seasons considered were rainy (November -April) and dry seasons (May -October).

RESULTS

Field effort and data collected

From July 2004 to April 2009, data were collected during 224 boat-based surveys. A total of 355 sightings of the targeted species were collected (92% of cetacean encounters around Mayotte), i.e. 195 for Stenella longirostris, 95 for Tursiops aduncus, 53 for Stenella attenuata and 12 for P. electra. The spatial distribution of observation effort and initial encounters is presented in Figures 2a andb. Overall, spatial coverage of effort was heterogeneous, but covered all available habitats around the island, both inside and outside the lagoon.

We collected biopsy samples from the four focal species from December 2004 to April 2009 (Stenella longirostris, n=28; Stenella attenuata, n=22; Tursiops aduncus, n=28 and Peponocephala electra, n=20) and from another delphinid species, the Fraser's dolphin (Lagenodelphis hosei, n=7), during a single and unique encounter (in association with a group of melon-headed whales) in January 2005. Seasonal distribution of sighting data and biopsy samples was balanced, allowing analyses of seasonal patterns of variation of habitat and stable isotope signatures (Table 2). For fish and stable isotope analyses in muscle samples, sample size was distributed al follows: Hemiramphus far (n=5); Mulloidichthys vanicolensis (n=5); Siganus argenteus (n=5); Scarus russelii (n=5) and Caranx melampygus (n=2).

Focal follows were performed on 33 groups of spinner dolphins (total time spent=37.1 h: n=466 behavioural sequences), 28 groups of Indo-Pacific bottlenose dolphins (total time spent=25.5 h; n=413 behavioural sequences) and 12 groups of pantropical spotted dolphins (total time spent=16.3 h; n=193 behavioural sequences). The melon-headed whale was not included in the behavioural budget analysis as sample size was too small (4 focal follows). Focal follows were undertaken all around the island.

Habitat differentiation

Table 3 presents distribution of the four species investigated in relation to environmental predictors. Table 4 presents correlations between variables. Only two variables were significantly correlated: distance from the coast and depth (P = 0.019). Density plots show that habitat of the four species were not well differentiated, apart along two habitat axes: depth and distance from the coast (Figure 3). For these variables, the Indo-Pacific bottlenose dolphin occurs significantly closer to the shore and in shallower waters, whereas the three other species are not well discriminated. For the MDS, axes 1 and 2 explained 78.4% and 21.2% of the variance, respectively (Figure 4). The plot slightly discriminated the Indo-Pacific bottlenose dolphin, but segregation among the other three species appeared relatively weak. The pairwise comparison (Wilcoxon tests) of species distribution for each variable provided more significant results. For depth, the three species occurring essentially outside the lagoon (S. longirostris, S. attenuata and P. electra) could not be discriminated (P > 0.05), while the T. aduncus significantly differed from the three others (all P < 0.001). Slope did not segregate any species. The variable "distance from the coast" significantly segregated T. aduncus from the three other species (all P < 0.0001). The variable "distance from the nearest reef" was significantly discriminant among the oceanic species: S. longirostris with S. attenuata (P = 0.002), S. longirostris with P. electra (P = 0.03).

For all variables, P. electra and S. attenuata were never discriminated (P > 0.05). In all delphinid species, no seasonal variation of habitat preferences was observed, for any variable (all P > 0.05).

Behavioural budgets

As we used four types of vessel for collecting behavioural data, we tested for a potential boat effect on the data but failed to find a significant difference (χ² = 3.238, df = 4; P = 0.569); therefore, subsequent analyses reflect a pooled data set. In Indo-Pacific bottlenose dolphins, the most frequent activities recorded were milling (32%), travelling (22%) and foraging (16%) (Figure 5). A quite similar pattern was also observed in the pantropical spotted dolphin, with travelling being the prevalent activity (32%), followed by milling (22%) and foraging (18%). In the spinner dolphin, socialising was the most commonly recorded behaviour (28%), followed by travelling (26%) and milling (22%) (Figure 5). Foraging behaviour was not observed in the spinner dolphin. Among the three species, significant differences in activity budgets were found (χ² = 177.33; df = 12; P < 0.0001). These differences were confirmed when performing pairwise comparisons: T. aduncus vs. S. longirostris (χ² = 137.50; df = 6; P < 0.0001), T. aduncus vs. S. attenuata (χ² = 53.42; df = 6; P < 0.001) and S. longirostris vs. S. attenuata (χ² = 109.18; df = 6; P < 0.0001).

Temporal variation of activity budgets

For all species, no significant variations of activity patterns were observed among seasons (H = 3.816; df = 3; P = 0.439). Contrastingly, behaviour patterns varied significantly according to time of day for Indo-Pacific bottlenose dolphin (χ² = 48; df = 5; P < 0.001), spinner dolphin (χ² = 13; df = 5; P = 0.002) and pantropical spotted dolphin (χ² = 11; df = 5; P = 0.009) (Figure 6, a to c). In Indo-Pacific bottlenose dolphins, foraging activities were prevalent during the morning and decreased throughout the day, whereas socializing was more frequent in the afternoon. In spinner dolphins, travelling activities increased along the day and social activities were more observed in the morning and the afternoon, whereas resting behaviour was more predominant around noon time. Finally, in pantropical spotted dolphins, feeding behaviour prevailed during the afternoon, along with travelling.

Activity budgets did not vary with water depth in

Stable isotope analyses

Stable isotope values of delphinids and fish were significantly different, as shown in figure 7.

The most apparent pattern was the higher trophic level of delphinids, reflected by higher δ 15 N values. In addition, δ 13 C values in delphinids were lower than in fish.

In delphinids, stable isotope values were lower in blubber than in skin. However, the pattern of differences observed between species was similar in both tissues (Figure 8 and9). The Fraser's dolphin shows a high marginality in comparison to the other species, with significantly higher δ 15 N values in the blubber (Figure 8). However, for skin values, overlap was observed with the melon-headed (Table 5). Overall, among species, significant differences in the skin existed for δ 15 N (H = 33.6; df = 2; P < 0.0001) and δ 13 C (H = 53.6; df = 1; P < 0.0001). For blubber, significant differences were also found for δ 15 N (H = 49.7; df = 1; P < 0.0001) and δ 13 C (H = 63; df = 1; P < 0.0001). A similar statistical difference among species for blubber tissue was found, even when excluding the Fraser's dolphin, very different to the four other species (H = 34; df = 2; P < 0.0001 for δ 15 N and H = 60; df = 2; P < 0.0001 for δ 13 C). The Indo-Pacific bottlenose dolphin had the greatest δ 13 C values, while the lowest values were observed in the Fraser's dolphin and in the two species of the genus Stenella. The melon-headed whale had intermediate values of δ 13 C, both for skin and blubber (Figures 8 and9). When looking at pairwise comparisons of δ 15 N and δ 13 C values in blubber and skin tissues, however, some degrees of overlap can be observed (Table 5). From skin samples, δ 15 N values were significantly different between species pairs, except between S. attenuata and T. aduncus and between L. hosei and P. electra (U-tests; P > 0.05). For δ 13 C values, overlap was evident between L. hosei and the two species of the genus Stenella. Finally, for blubber tissue, δ 15 N values were significantly different between species pairs, except between S. attenuata and P. electra and between S. attenuata and T. aduncus. δ 13 C values showed the highest degrees of overlap, particularly between S. longirostris and S. attenuata, between S. attenuata and L. hosei, as well as between L. hosei and P. electra (U-tests; P > 0.05; Table 5). Stable isotope values from fish samples were useful in order to provide a context to interpret values in delphinids. Among fish, significant differences were observed for δ 13 C (H = 11.2; df = 4; P = 0.02) and δ 15 N (H = 11.6; df = 4; P = 0.01). Siganus argenteus and Scarus russelii (herbivores) had the lowest trophic position (δ 15 N), while Caranx melampygus, the most predatory species, had the highest trophic level (δ 15 N). Their foraging habitats were also well discriminated, with Mulloidichthys vanicolensis having the highest δ 13 C values and Caranx melampygus with Hemiramphus far the lowest (Figure 7). These latter were about 0.5-1.5 ‰ δ 13 C and 3-4 ‰ δ 15 N lower than T. aduncus.

Seasonal variations of stable isotope signatures were observed in all species for skin and blubber tissues (Table 6). In the two species of the genus Stenella and the melon-headed whale, δ 13 C values were more negative during the rain season. A reverse situation was observed in T. aduncus during the rain season; δ 15 N values were decreasing for Stenella dolphins and P. electra, while increased in T. aduncus. However, while (sometimes) statistically significant in some cases, seasonal variations appear to be relatively limited.

DISCUSSION

General

This work represents a detailed study on habitat and resource segregation among tropical dolphins around Mayotte, in the southwest Indian Ocean. It integrates several methods implemented over four years, with varying temporal resolutions: from instantaneous sighting data and behavioural observations collected during daylight hours to stable isotope analyses in skin that represents the foraging niche over days or in the blubber which integrates stable isotope signatures over months [START_REF] Abend | Differences in ratios of stable isotopes of nitrogen in long-finned pilot whales (Globicephala melas) in the western and eastern North Atlantic[END_REF]. The indicators were selected for their ability to document the main dimensions of the ecological niche along which segregation might occur: physiographic characteristics describe the spatial dimension of the ecological niche, carbon isotopic signature focuses on the coastal-offshore gradient of the foraging niche, nitrogen isotopic signature expresses the resource dimension of the niche, and the daily activity budget deals with temporal dimension.

Overall, the main finding of this work is that none of the indicators of trophic niche dimensions, examined solely, reveals complete ecological segregation amongst the four species studied, but the combination of all indicators do (Table 7). Hence, physiographic characteristics of habitats used by the dolphins during daylight, when visual observations were possible, only allow the Indo-pacific bottlenose dolphin to be differentiated from the others. Carbon isotope signatures allow the melon-headed whale to be separated from the Stenella dolphins. Finally nitrogen isotopic signature and activity budget identify differences between spinner and pantropical spotted dolphins.

Identifying the limitations of the study is necessary for delineating its validity range. Most daily field trips were undertaken from Mayotte main harbour located on the east coast of the island and were limited to daylight hours. Hence, effort was concentrated in the lagoon and the vicinity of outer slope of barrier reef, within the 1000 m isobath, and nocturnal distribution and activity could not be documented. The resource dimension of the niche was documented in a very integrated way, as C and N isotopic contents of a predator express foraging habitat and trophic level but not diet per se, which is only documented by sporadic direct observations when no biological material is available. Also, in stable isotopes analyses, as in most studies relying on the use of ecological tracers transmitted via food (e.g. fatty acids, contaminants, heavy metals), only differences in stable isotope contents are really informative, whereas similarities may result from a variety of prey combinations. Finally, behavioural budget data is limited by our capacity to infer dolphin underwater activity from surface events. In particular, foraging, which is the key activity to consider when investigating segregation mechanisms, can either be associated to no or barely visible surface events or to explicit and often highly dynamic ones. Nonetheless, in a multifaceted approach as the one followed here, the limitations of each indicator tend to be compensated by the others. For instance, stable isotope analyses reveal foraging habitat and trophic level of prey eaten day and night over the past few days or months, which is extremely useful to disentangle the inherent ambiguities of observations limited to daylight hours.

Conversely, behavioural data can help identify differences in foraging strategies that cannot be found in stable isotope analyses.

The ecological significance of these indicators will now be interpreted sequentially from those related to the spatial, the resource and finally the temporal dimensions of the ecological niche.

Spatial segregation inferred from direct observations and δ 13 C signatures

This study confirms that T. aduncus is associated with coastal, shallow water and reef habitats. Its ecological niche clearly differs spatially from the other species of the community. Coastal foraging habitats are confirmed by high δ 13 C value indicating a benthic carbon source that is primarily available in coastal environments [START_REF] France | Carbon-13 enrichment in benthic compared to planktonic algae: foodweb implications[END_REF][START_REF] Hobson | Tracing origins and migration of wildlife using stable isotopes: a review[END_REF]. Preference for coastal habitat is reported throughout species range, a preference shared with the Indo-pacific humpback dolphin, Sousa chinensis, which is present in very low numbers around Mayotte, but could not be considered in this work.

S. longirostris and S. attenuata co-occur in waters along the outer slope of the barrier reef around Mayotte. They overlap extensively, but the latter tends to occur in deeper waters, located further offshore. Low δ 13 C values found in both species, with extensive overlap, are in line with foraging habitats located outside the lagoon at epipelagic depths; this interpretation is reinforced by the overlap also found between the two Stenella and the Fraser's dolphin, a typically oceanic dolphin. The pattern observed around Mayotte has also been reported from other insular populations, such as off La Réunion where spotted dolphins occur in deeper, more offshore waters than spinners do [START_REF] Dulau-Drouot | Cetacean diversity off La Réunion Island (France)[END_REF]. These two species are not restricted to periinsular waters; instead populations of the two species also dwell in the open ocean [START_REF] Wade | Estimates of dolphin abundance in the eastern tropical Pacific: preliminary analysis of five years of data[END_REF][START_REF] Ballance | Cetaceans of the western tropical Indian Ocean: distribution, relative abundance and comparison with cetacean communities of two other tropical ecosystems[END_REF]. In this situation, extensive overlap in preferred habitat is also observed, as reported from the western South Atlantic and the eastern tropical Pacific [START_REF] Polachek | Relative abundance, distribution and inter-specific relationship of cetacean shools in the eastern tropical Pacific[END_REF][START_REF] Moreno | Distribution and habitat characteristics of dolphins of the genus Stenella (Cetacea: Delphinidae) in the southwest Atlantic Ocean[END_REF].

For melon-headed whale, a fairly limited number of sightings were collected, allowing only a partial description of its habitat preferences. Nevertheless, habitat physiographic characteristics of P. electra as documented in this work were significantly different from S. longirostris, but could not be differentiated from S. attenuata. The melon-headed whale has a more oceanic distribution than the other species [START_REF] Brownell | Behavior of melon-headed whales, Peponocephala electra, near oceanic islands[END_REF]) and it is unknown whether the groups seen around Mayotte are mostly oceanic dwellers that occasionally visit peri-insular waters or if they display some group-specific preference for the peri-insular slope, a habitat that they could exploit around all islands, reefs and seamounts from off the northern end of Madagascar to Grande Comore (western Comoros archipelago). Quite interestingly, δ 13 C values measured in the melon-headed whale are intermediate between the epi-to mesopelagic Stenella and Fraser's dolphins and the coastal dwelling Indo-pacific bottlenose dolphin, rather than being identical to the Stenella carbon isotopic content as could be expected from the similarity found in physiographic characteristics. This would suggest spatial segregation along a vertical axis, with melon-headed whales foraging deeper and hence closer to detritic carbon sources than Stenella spp. do.

Resource partitioning inferred from δ 15 N signatures

Nitrogen isotopic signatures are the main source of information on resource utilization by dolphins in Mayotte. Direct evidences of resource use are limited to some anecdotal observations of prey hunting or capture by T. aduncus and S. attenuata (Kiszka & Pusineri, unpublished observations). Carbon isotopic signatures of fish collected in the lagoon also convey some contextual information on plausible prey for the more coastal dolphins. Finally, when comparing trophic levels inferred from δ 15 N signatures one should only consider in the comparison dolphin species with overlapping δ 13 C signatures, i.e. living in the same habitat, because reference levels of δ 15 N in oceanic vs. lagoon habitats are unknown.

Indo-pacific bottlenose dolphins isotopic content can be compared to the isotopic values of putative prey fish collected in the lagoon. The herbivorous fish Siganus argenteus and Scarus russelii have δ 13 C values 2-4 ‰ higher than T. aduncus, and would therefore unlikely be important components of its diet. In contrast, blue trevally Caranx melampygus and blackbarred halfbeak Hemiramphus far display carbon and nitrogen signatures about 0.5-1.5 ‰ and 3-4 ‰ lower respectively than T. aduncus; such differences fit well with an enrichment of one trophic level. Therefore, these two predatory fish would be plausible major prey for T. aduncus.

Anecdotal direct observations in Mayotte are in line with this interpretation even if other fishes, like the mullet Mulloidichthys vanicolensis, were also observed being preyed upon (Kiszka & Pusineri, unpublished observations). Our result is also consistent with existing information on the diet of the Indo-Pacific bottlenose dolphin in the region (Zanzibar, Tanzania), suggesting this species forages on a large number of prey species, especially reef fish [START_REF] Amir | Feeding ecology of the Indo-Pacific bottlenose dolphin (Tursiops aduncus) incidentally caught in the gillnet fisheries off Zanzibar, Tanzania[END_REF].

Elsewhere, T. aduncus is known to feed on fish species that do not aggregate in large schools [START_REF] Mann | Cetacean Societies -Field Studies of Dolphins and Whales[END_REF].

The two Stenella have largely overlapping ranges of both physiographic habitats and carbon isotopic contents, even if S. attenuata is seen slightly further offshore and is nonetheless slightly carbon-enriched (higher δ 13 C ratios). According to δ 15 N values, pantropical S. attenuata are on average 1.5‰ higher than spinner dolphins, i.e. half a trophic level, which would express some degree of niche segregation between the two species. In addition to this, S. attenuata seems to have a wider niche breadth than S. longirostris. Pantropical spotted dolphins have been frequently observed feeding close to the barrier reef where their prey aggregate (fishes of the genus Exocoetidae; Kiszka & Pusineri, unpublished observations), in agreement with their slightly higher δ 13 C signature. Fine-scale processes allowing niche differentiation between the two Stenella species have also been found in other regions, such as in the eastern tropical Pacific [START_REF] Perrin | Stomach content of porpoise, Stenella spp., and yellowfin tuna, Thunnus albacares, in mixed-species aggregations[END_REF]. Spinner dolphins there are reported to feed at night upon scattering-layer organisms, i.e. on vertically migrating mesopelagic fishes, cephalopods and crustaceans, caught in the upper 200 m and occasionally as deep as 400 m [START_REF] Perrin | Stomach content of porpoise, Stenella spp., and yellowfin tuna, Thunnus albacares, in mixed-species aggregations[END_REF][START_REF] Norris | The Hawaiian Spinner Dolphin[END_REF][START_REF] Dolar | Comparative feeding ecology of spinner dolphins (Stenella longirostris) and Fraser's dolphins (Lagenodelphis hosei) in the Sulu Sea[END_REF]. Conversely, pantropical spotted dolphins would feed day and night on epipelagic fishes and cephalopods [START_REF] Perrin | Stomach content of porpoise, Stenella spp., and yellowfin tuna, Thunnus albacares, in mixed-species aggregations[END_REF].

Melon-headed whales were observed in much the same habitats as pantropical spotted dolphins, but comparatively higher δ 13 C values suggested vertical segregation could occur. Nitrogen isotopic content further suggests a slightly higher trophic level (about 1/3 trophic level). Earlier works report mesopelagic fishes and cephalopods, supposedly preyed upon in the upper 700 meters, as the main component of its diet [START_REF] Young | Vertical distribution and photosensitive vesicles of pelagic cephalopods from Hawaiian waters[END_REF][START_REF] Brownell | Behavior of melon-headed whales, Peponocephala electra, near oceanic islands[END_REF]). An element of comparison is provided by the Fraser's dolphin, which was added to the study in an attempt to provide isotopic reference for a true oceanic predator; in addition to this, the species is frequently observed forming mixed group with melon-headed whale [START_REF] Jefferson | Peponocephala electra[END_REF][START_REF] Kiszka | Cetacean diversity around the Mozambique Channel island of Mayotte (Comoros Archipelago)[END_REF][START_REF] Dulau-Drouot | Cetacean diversity off La Réunion Island (France)[END_REF]. Not surprisingly, Fraser's dolphins displayed the second lowest δ 13 C values, in agreement with their oceanic lifestyle, and the highest δ 15 N values, that fit well with the higher trophic level, likely associated with its preference for larger prey already reported elsewhere [START_REF] Dolar | Comparative feeding ecology of spinner dolphins (Stenella longirostris) and Fraser's dolphins (Lagenodelphis hosei) in the Sulu Sea[END_REF]. Studies of stomach contents from the Pacific suggest this species feeds on relatively large mesopelagic fish and cephalopods from near the surface to probably as deep as 600 meters [START_REF] Robison | Mesopelagic fishes eaten by Fraser's dolphin, Lagenodelphis hosei[END_REF][START_REF] Dolar | Comparative feeding ecology of spinner dolphins (Stenella longirostris) and Fraser's dolphins (Lagenodelphis hosei) in the Sulu Sea[END_REF]. In Mayotte, Fraser's dolphins and melon-headed whales, although generally seen associated, do not overlap in their isotopic niches, the latter being more δ 13 C enriched than the former, which could be interpreted as feeding a deeper food source, possibly associated to peri-insular slopes, whereas the Fraser's dolphin would rely on large epi-to-mesopelagic truly oceanic prey. These two species might associate for other reasons than foraging, such as social advantage or vigilance against predators.

Temporal segregation inferred from seasonal patterns and activity budgets

Our study did not reveal seasonal variations of occurrence or habitat preferences as based on the analyses of visual observations; this could be linked to the absence of seasonal variability in tropical environments. On the other hand, stable isotope values displayed significant differences between dry and rain seasons in all species. Oceanic species, i.e. spinner, pantropical spotted dolphins and melon-headed whales, showed similar levels of variation. Conversely, the Indo-Pacific bottlenose dolphin differed. This species only foraged in the lagoon, and, during the rainy season, δ 13 C values were enriched, which could be linked to increasing hydrodynamic activity and remobilisation of benthic sources of carbon in the lagoon. It is therefore suggested that all species have the same habitat use year-round, but isotopic content can vary seasonally as a result of hydro-climatic processes.

At a finer time scale, segregation mechanisms could rely on differential daily activity budgets between species; this aspect was investigated in the bottlenose and the two Stenella dolphins, but not in the melon-headed whale. Foraging activities of Tursiops aduncus were observed throughout the day, but more frequently in the morning, closer to shore. A similar pattern was observed in common bottlenose dolphins (Tursiops truncatus) in Florida [START_REF] Shane | Behaviour and ecology of the bottlenose dolphin at Sanibel Island, Florida[END_REF].

Pantropical spotted dolphins feed during daylight, with an increase in feeding activity along the day. Nocturnal feeding is not excluded for these two species but could not be accessed directly.

Spinner dolphins would only feed at night as foraging was never observed during daylight hours.

Behavioural ecology of spinner and pantropical spotted dolphins around Mayotte is similar than in other areas, including around Hawaii and in the oceanic eastern tropical Pacific [START_REF] Perrin | Stomach content of porpoise, Stenella spp., and yellowfin tuna, Thunnus albacares, in mixed-species aggregations[END_REF][START_REF] Norris | The Hawaiian Spinner Dolphin[END_REF]. Our results underline a clear pattern of niche segregation along the time dimension, at least during the day.

Final comments

Three main dimensions define the ecological niche of a species: habitat, diet and time. Our study integrated these three axes to investigate ecological niche segregation among the delphinid community found around Mayotte. Habitat has been assessed through the investigation of the relationships between delphinid distribution and environmental variables (particularly physiography). Trophic level and foraging habitat have been assessed indirectly, through the use of stable isotopes of N andC respectively (De Niro &Epstein 1978, Kelly 2000). Finally, the temporal component of the ecological niche has been integrated through the study of behavioural budgets, especially their diurnal variations that may potentially segregate species' ecological niche. The use of multiple approaches (habitat, behaviour and feeding ecology studies) was most useful to investigate ecological niche segregation, especially when looking at closely related species within a common restricted range. We hypothesize a conceptual scheme of resource partitioning inferred from these measurements:

1 -The Indo-pacific bottlenose dolphin is mostly confined to the inner lagoon or at least in shallow reef-associated habitats. They feed diurnally (possibly nocturnally as well, although this could not be documented), with daily routines that would follow variation in prey catchability during the day, e.g. mullet being often caught close to the coast in the morning, and Caranx melampygus and Hemiramphus far the rest of the day across the lagoon; 2 -The spinner dolphin lives in outer reef habitats and forage only nocturnally on small mesopelagic prey; 3 -The pantropical spotted dolphins also lives in outer reef habitats, that largely overlap with the spinner dolphins but feed at least partly diurnally and at dawn on epipelagic prey, that include flying fish caught closer to the barrier reef; 4 -The melon-headed whale is seen in the same habitat as pantropical spotted dolphins, but forages deeper over the peri-insular slope. This ecological segregation is more significant than in other communities, such as in some epipelagic seabirds [START_REF] Ridoux | The diets and dietary segregation of seabirds at the subantarctic Crozet Islands[END_REF][START_REF] Cherel | Resource partitioning within a tropical seabird community: new information from stable isotopes[END_REF]. Conversely, in diving predators such as large pelagic fish and dolphins, ecological niche segregation is clearly distinguishable [START_REF] Potier | Feeding partitioning among tuna taken in surface and mid-water layers: the case of yellow fin (Thunnus albacares) and bigeye (T. obesus) in the western tropical Indian Ocean[END_REF][START_REF] Ménard | Isotopic evidence of distinct feeding ecologies and movement patterns in two migratory predators (yellowfin tuna and swordfish) in the western Indian Ocean[END_REF][START_REF] Praca | Ecological niches of three teuthophageous odontocetes in the northwestern Mediterranean Sea[END_REF]. This could be related to the low spatial structure of marine ecosystems in tropical and oligotrophic areas. Conversely, clear isotopic and resource-related gradients can be found in subpolar and polar environments over large spatial scale [START_REF] Jaeger | Etude isotopique des variations saisonnières et à long terme de l'écologie alimentaire des oiseaux marins de l'océan austral[END_REF] as well as vertically (including in the tropics), at a small spatial scale in the water column (this study). This vertical gradient is accessible to fish and dolphins, and not in epipelagic seabirds.

Improvement in our understanding of resource partitioning mechanisms among Mayotte delphinids may be obtained in several directions: -investigating acoustically dolphins' nocturnal distribution and activity; -documenting the regional isoscape by analyzing carbon and nitrogen isotopic composition in phytoplankton collected along a coastal-offshore gradient and along a vertical gradient as well; -investigating residency patterns of dolphin groups living around Mayotte by using photo-identification or individual telemetry approaches, in order to establish whether they are genuinely associated to these peri-insular structures, or have a more oceanic lifestyle, occasionally approaching islands. Table 1: Methodology used to explore the three main dimensions of the ecological niche in the present study.

Tables

Niche dimensions Variables tested

Habitats Resources Time Distribution and associated habitat characteristics

Habitat defined on physiographic aspects (and seasonal variation)

Activity budget

Daily activity rhythm (and seasonal variation)

N isotopic signature Trophic level (and seasonal variation)

C isotopic signature

Habitat along a coastaloceanic gradient (and seasonal variation)

  Indo-Pacific bottlenose dolphin (H = 2.060; df = 4; P = 0.725), spinner dolphin (H = 5.621; df = 4; P = 0.229) and pantropical spotted dolphin (H = 8.049; df = 4; P = 0.09). However, activity budget varied with distance from the coast for Indo-Pacific bottlenose dolphin (H = 9.542; df = 4; P = 0.04; especially increasing foraging activity closer to shore), although not for either spinner (H = 3.251; df = 4; P = 0.517) or pantropical spotted dolphins (H = 4.201; df = 4; P = 0.379). Feeding activities of the Indo-Pacific bottlenose dolphin increased with decreasing distance from coast.

Figure captions Fig. 1 :

 captions1 Figure captions

Fig. 2 :

 2 Fig. 2: Spatial distribution of effort (per 1 km cell) and distribution of dolphin sightings around

Fig. 4 :

 4 Fig. 4: Metric Multi-Dimensional Scaling plot of delphinid habitat in relation to physiographical

Fig. 5 :

 5 Fig. 5: Overall activity budgets for Indo-Pacific bottlenose dolphins, pantropical spotted dolphins

Fig. 6 :

 6 Fig. 6: Within day variations of behavioural budget in spinner dolphin (a), Indo-Pacific

Fig. 7 :

 7 Fig. 7: Mean (with Standard Errors in bold line and Standard Deviation in thin line) stable isotope values in dolphin skin and fish muscle (δ 13 C and δ 15 N in ‰).

Fig. 8 :

 8 Fig. 8: Mean (with Standard Errors in bold line and Standard Deviation in thin line) stable

Fig. 9 :

 9 Fig. 9: Mean (with Standard Errors in bold line and Standard Deviation in thin line) stable

  Fig. 1

Table 2 :

 2 Seasonal distribution (winter/dry season: May to October; summer/rain season: November to April) of sighting data and biopsy samples collected from December 2004 to April 2009.

		SIGHTINGS	BIOPSIES
	Species	Winter Summer Winter Summer
	Tursiops aduncus	48	43	12	16
	Stenella longirostris	101	67	12	13
	Stenella attenuata	13	23	12	10
	Peponocephala electra	5	6	10	10
	Lagenodelphis hosei	0	1	0	7

Table 3 :

 3 Distribution of the delphinids in relation to physiographic variables.

		Mean	Median	SD	Q1-Q3
	Depth (meters)
	Tursiops aduncus	47.9	32	70.2	19.7 -45.1
	Stenella longirostris	220.7	230.5	175.9	71.7 -285.7
	Stenella attenuata	301.4	276.4	277.2	44.9 -370.4
	Peponocephala electra	486.5	462.2	244.1	274.9 -666.2
	Slope (degree)
	Tursiops aduncus	2.1	0	6.7	0 -0.8
	Stenella longirostris	9.5	0	17.9	0 -7.9
	Stenella attenuata	9.7	0	19.7	0 -2.9
	Peponocephala electra	13.6	0	20.4	0 -39.8
	Distance coast
	(meters)
	Tursiops aduncus	2001.8	996.2	2169.8	487.6 -2778.5
	Stenella longirostris	5258.8	5068.3	2763.5	3216.6 -7191.7
	Stenella attenuata	6295.3	5771.8	3331.2	3921.7 -8602.8
	Peponocephala electra	6665.6	7086.4	2130.7	5369.9 -8520.4
	Distance reef (meters)
	Tursiops aduncus	1363	509.2	2071.8	268.7 -1474.1
	Stenella longirostris	1059.7	700.4	1144.8	478 -1320.6
	Stenella attenuata	2011.8	1210	1999.5	722 -2701.9
	Peponocephala electra	2452.1	1363.3	3014.8	927.6 -2563.8

Table 4 :

 4 Correlation between variables (Pearson's correlation values above the diagonal and associated P values below the diagonal).

		Distance coast	Distance reef	Slope	Depth
	Distance coast	-	0.27	-0.13	0.01
	Distance reef	< 0.001	-	0.13	0.34
	Slope	0.020	0.028	-	0.24
	Depth	< 0.001	0.731	< 0.001	-

Table 5 :

 5 Pairwise Mann-Whitney U test p values for each pairs of delphinid species in carbon and nitrogen in skin and blubber.

	SKIN
	Nitrogen	Lagenodelphis hosei Peponocephala electra Stenella attenuata Stenella longirostris
	Tursiops aduncus	0.01	0.001	0.125	< 0.001
	Stenella longirostris	< 0.001	< 0.001	< 0.001
	Stenella attenuata	0.01	0.009
	Peponocephala electra	0.232
	Carbon
	Tursiops aduncus	< 0.001	< 0.001	< 0.001	< 0.001
	Stenella longirostris	0.339	< 0.001	0.02
	Stenella attenuata	0.157	0.008
	Peponocephala electra	0.008
	BLUBBER
	Nitrogen
	Tursiops aduncus	< 0.001	0.022	0.08	< 0.001
	Stenella longirostris	< 0.001	< 0.001	< 0.001
	Stenella attenuata	< 0.001	0.485
	Peponocephala electra	< 0.001
	Carbon
	Tursiops aduncus	< 0.001	< 0.001	0.001	0.001
	Stenella longirostris	0.02	< 0.001	0.182
	Stenella attenuata	0.242	< 0.001
	Peponocephala electra	0.112

Table 6 :

 6 Pairwise Mann-Whitney U test P values for seasonal differences of stable isotope signatures for each species and type of tissue.

	SPECIES/SEASON	BLUBBER	SKIN
	Tursiops aduncus
	Dry season	P < 0.001	P < 0.001
	Rainy season	P > 0.05	P > 0.05
	Peponocephala electra
	Dry season	P > 0.05	P > 0.05
	Rainy season	P > 0.05	P < 0.01
	Stenella attenuata
	Dry season	P > 0.05	P > 0.05
	Rainy season	P < 0.05	P < 0.001
	Stenella longirostris
	Dry season	P > 0.05	P < 0.05
	Rainy season	P > 0.05	P > 0.05

Table 7 :

 7 Summary of results obtained for the four indicators of ecological niche dimensions among the Mayotte delphinid community. Different letters denote species that segregate for the indicator being considered.

	Indicators of niche	Tursiops	Stenella	Stenella	Peponocephala
	dimensions		aduncus	attenuata	longirostris	electra
	Physiographic characteristics	A		B
	Carbon values	isotopic	A	B		C
	Nitrogen values	isotopic	A		B	C
	Daily activity rhythm	A	B	C	Not investigated

Acknowledgements

The data from July 2004 to June 2006 were collected during a dolphin research project conducted by the Office National de la Chasse et de la Faune Sauvage (ONCFS, Game and Wildlife Service) and the Agriculture and Forestry Office (Direction de l'Agriculture et de la Forêt). From May 2007 to April 2009, data were collected during a joined programme of the University of La Rochelle, the Office National de la Chasse et de la Faune Sauvage and the Collectivité Départementale de Mayotte. Funding was provided by the Conseil Général de Mayotte, the Ministère de l'Energie, l'Ecologie, le Développement Durable et de la Mer (MEEDDM), the University of La Rochelle and the Office National de la Chasse et de la Faune Sauvage. We thank Robin Rolland, Alban Jamon, Ismaël Ousseni, Julien Wickel (DAF), Sarah Caceres, Franck Charlier, Denis Girou (ONCFS), Didier Fray (CDM) and the personnel of Brigade Nature (CDM and ONCFS) for assistance in the field in Mayotte. Thanks are also addressed to Karim Layssac (Service des Affaires Maritimes de Mayotte) for his contribution in the collection of fish samples. We are also grateful to Gaël Guillou (University of La Rochelle) for running the mass spectrometer analyses. The authors also thank Giovanni Bearzi (Tethys Research Institute, Italy), Mariana Degrati (Marine Mammal Laboratory, Argentina), Karen Stockin (Marine Coastal

Group, Massey University, NZ) and Florence Caurant (University of La Rochelle) for their constructive comments on the manuscript. The authors also would like to thank the four anonymous reviewers, who provided constructive comments that greatly improved our manuscript.


Jérôme Malick
email: jerome.malick@inria.fr

Frédéric Roupin
email: frederic.roupin@cnam.fr

Numerical Study of Semidefinite Bounds for the k-cluster Problem

Keywords: combinatorial optimization, semidefinite programming, k-cluster, Lagrangian duality and relaxation

Numerical Study of Semidefinite Bounds for the k-cluster Problem

Introduction, motivations

Many studies have shown the power, as well as the limitations, of semidefinite programming (SDP) in combinatorial optimization. Regarding bounding and exact resolution, the current statement of fact is, roughly speaking, that semidefinite programming bounds are of very good accuracy, but demand much time to be computed. This drawback prevents in general their use in exact resolution procedures, as branch-and-bound algorithms.

The particular problem "k-cluster" (see forthcoming (1)) is well-suited for a resolution using SDP, thought. Indeed this classical combinatorial optimization problem does not admit a polynomial time approximation scheme, and it is known to be NP-hard even in very special cases (see e.g. [START_REF] Liazi | A constant approximation algorithm for the densest k-subgraph problem on chordal graphs[END_REF] for recent results and references). The point is that linear bounds for this problem are inaccurate so that they can hardly be used directly in an exact resolution procedure. So in practice, one of the most efficient general exact resolution methods for k-cluster problems is based on convex quadratic programming combining an SDP solver with CPLEX [START_REF] Billionnet | Improving the performance of standard solvers for quadratic 0-1 programs by a tight convex reformulation: The qcr method[END_REF].

The recent work [START_REF] Malick | Solving k-cluster problems to optimality using adjustable semidefinite programming bounds[END_REF] presents a SDP-like bound for k-cluster which keeps nice features of the SDP bounds, while gaining in computation time. We sketch the approach in section 4. The driving idea is to trade computing time for a (small) deterioration of the quality of the bound. The new SDP-like bound reaches indeed a good balance between tightness and speed of computation. A basic branch-and-bound algorithm using this bound have been shown to have very good performances and to be competitive with [START_REF] Billionnet | Improving the performance of standard solvers for quadratic 0-1 programs by a tight convex reformulation: The qcr method[END_REF].

The goal of this paper is to give a numerical comparison of these two bounds (respectively the standard and the new SDP bound), in terms of tightness, computation time, and balance between both. The computing time depends obviously on the solvers, and in turn the performance of the solvers depends on the formulation of the bounds. So we consider two SDP solvers known to be very efficient, and two equivalent formulations of the SDP bound (each of both turns out to advantage one solver). The conclusion of this numerical work is that the new SDP bound has an advantageous trade-off between accuracy and speed of computation in view of embedding it inside of a branch-and-bound.

k-cluster problem

We briefly introduce the k-cluster problem, the combinatorial optimization problem we consider in this work. We refer to the recent [START_REF] Liazi | A constant approximation algorithm for the densest k-subgraph problem on chordal graphs[END_REF] for complete references, and to [START_REF] Jäger | Improved approximation algorithms for maximum graph partitioning problems[END_REF] for discussions on the SDP approach for this problem.

Consider an undirected, weighted graph G = (V, E) of n vertices v 1 , . . . , v n , and a non-negative weight w ij on each edge (v i , v j ). For a given integer k in {1, . . . , n}, the k-cluster problem consists in determining a subset S of k vertices such that the total edge weight of the subgraph induced by S is maximized. In unweighted graphs, the problem is also called the densest subgraph problem. Denoting W = (w ij ) ij the weight-matrix of the graph G, the problem can be written as a {0, 1}-quadratic optimization problem with one linear constraint max 1 2

x ⊤ W x :

n i=1 x i = k, x ∈ {0, 1} n . ( 1
)
As mentioned in the introduction, this problem is well-suited for a SDP approach. We study here the quality of different SDP relaxations of this problem.

Lagrangian and semidefinite relaxations

Lagrangian duality is a mechanical way to create bounds. The choice of the constraints to be dualized gives different bounds. The dualization of all the constraints for a {0, 1}-quadratic problem leads naturally to a SDP problem (see e.g. the pedagogical [START_REF] Lemaréchal | Semidefinite relaxations and Lagrangian duality with application to combinatorial optimization[END_REF]). The best bound θ B obtained by duality for {0, 1}-quadratic optimization is the one given by the dualizing only the 0, 1constraints. This bound however does not lead to a SDP problem directly. The usual technique is to add redundant constraints to the problem until the total dualization (corresponding to a SDP problem) tights the best bound θ B .

General results [START_REF] Faye | Partial lagrangian for general quadratic programming. 4'OR[END_REF] applied to the {0, 1}-quadratic problem (1) give that both the following constraint sets to be dualized lead to θ B :

• all the "product" constraints x j ( n i=1 x i -k) = 0 for all j in {1, . . . , n}, • just the "squared" constraint ( n i=1 x i -k) 2 = 0. The next theorem formalizes this result while operating at the same time several transformations: namely the change of variable {0, 1} → {-1, 1}, the homogenization of linear terms in R n and the lifting in n+1 symmetric matrix space S n+1 . The notation we need is the following. Define the n+ 4 symmetric (n + 1) × (n + 1)-matrices Q, Q S , and Q j (for j ∈ {0, . . . , n}) by

Q := 1 4   e ⊤ W e e ⊤ W W e W   , Q 0 :=   0 e ⊤ e 0   , Q j :=   0 ẽ⊤ j ẽj C j   and Q S =   0 0 0 ee ⊤   + (n -2k)Q
0 where e is the vector of all ones, ẽj = e + (n -2k)e j is the vector of R n made up from e and e j the j + 1-th element of the canonical basis of R n , and the jth line/column of C j equals e + e j (all other elements are 0). Moreover E i is the (n + 1) × (n + 1) matrix with zero entries everywhere but in position (i, i) where there is a one. Recall that the inner-product in matrix space

S n+1 is X, Y = n+1 i,j=1 X ij Y ij = trace(XY ), then set A P (X) := Q 0 , X , . . . , Q n , X , E 0 , X , . . . , E n , X ∈ R 2n+2 A S (X) := Q S , X , E 0 , X , . . . , E n , X ∈ R n+2 b P := (4k -2n, . . . , 4k -2n, 1, . . . , 1) ∈ R 2n+2 b S := ((2k -n) 2 , 1, . . . , 1) ∈ R n+2 Theorem 3.1 (Equivalent SDP relaxations of k-cluster) The two SDP problems max { Q, X : A P (X) = b P , X 0} (2) max { Q, X : A S (X) = b S , X 0} (3)
have the same optimal value, which corresponds to θ B , the relaxation of the {0, 1} constraint in (1). We call it the SDP bound for k-cluster. Adding any redundant quadratic equalities to (1) cannot enhance this bound.

Proof. The equivalence of the SDP relaxation of (1) with the product constraints on one hand, and with the squared constraint on the other hand comes from [FR07, Prop. 5], as well as the optimality among addition of any redundant equality constraints. The fact that the transformations lead to those SDP is detailed in [START_REF] Malick | Solving k-cluster problems to optimality using adjustable semidefinite programming bounds[END_REF] the case with product constraints. The case with the squared constraint follow the same way. ✷

To compute the SDP bound for k-cluster (that is, to solve (2) or (3)), we use solvers that are known to be among the most efficient and reliable:

• CSDP [START_REF] Borchers | Csdp, a c library for semidefinite programming[END_REF], which implements an interior-point method,

• SB, based on the spectral method [START_REF] Helmberg | A spectral bundle method for semidefinite programming[END_REF], which is much used in the context of combinatorial optimization.

The efficiency of these solvers depends on the formulation of the SDP bound.

In Section 5, we will take the best choices to have a fair comparison with the alternative bound and solver, that we present in the next section.

The recent work [START_REF] Malick | Solving k-cluster problems to optimality using adjustable semidefinite programming bounds[END_REF] introduces the following SDP-like bounds for the k-cluster problem. For a given α > 0, we define Θ P (α) by

Θ P (α) = α 2 n 2 + 1 2α Q 2 - α 2 min X -Q/α 2 : A P (X) = b P , X 0 . (4)
We similarly define Θ S (α) with A S and b S ; the properties we discuss below hold for both Θ P (α) and Θ S (α). Regarding our objectives (explained in the introduction), Θ P (α) has interesting features. [START_REF] Malick | Solving k-cluster problems to optimality using adjustable semidefinite programming bounds[END_REF] shows that Θ P (α) is a bound for (1) which is weaker that the SDP bound, but can still get arbitrarily close to SDP:

Θ P (α) ≥ val (2) and Θ P (α) -→ α→0 val (2).
Moreover, it turns out that solving (4) is easier to solve than (2) (for α not too small). The quadratic SDP problem inside of (4) is a particular semidefinite least-squares problem; so we call Θ P (α) and Θ S (α) the SDLS bounds. These problems have nice geometrical properties and admit an efficient resolution, even for large-scaled problems; we have implemented an solver adapted and tuned for solving (4) using the approach presented in [START_REF] Malick | A dual approach to semidefinite least-squares problems[END_REF]. Of course, computing Θ P (α) gets harder when α → 0. In practice, we fix the value α = 10 -4 for which our solver has no problem while the obtained bound Θ P (α) is almost as accurate as the SDP bound (2). There is still a difference which is notified in the tables of results in relative value in the column "gap(%)".

Numerical results

The numerical experiments aim at comparing:

• the usual SDP bound for k-cluster (2) or (3) computed by CSDP and SB,

• the new bound Θ P (α) or Θ S (α) computed by our solver, called SDLS.

We consider randomly generated instances of k-cluster (test problems already used for similar testing [START_REF] Billionnet | Improving the performance of standard solvers for quadratic 0-1 programs by a tight convex reformulation: The qcr method[END_REF]). We use a Pentium IV 2.2 GHz with 1 Go of RAM under Linux.

In Table 1, we compare the solvers on each of the two formulations: the one with the product constraints and the one with the squared constraint. For CSDP, the computing time depends on the number of constraints, so (3) is solved about ten times faster than (2) when n = 100. On the other hand, (2) generally provides the best results for SB (except for large values of k or in dense graphs), as for Θ P (α). Hence, the best formulation of the semidefinite bounds is not always the one with fewer constraints.

In Table 2, we keep only the best formulation-software combination. Since the stopping criteria are different for the solvers, we indicate the time required by SB and CSDP to achieve the SDLS bound. The values of the stopping criteria are: 10 -7 for SDLS, 10 -4 for SB, and 10 -5 for CSDP. Table 2 shows that our solver is fast. Moreover, the decrease of the bound at each iteration during the run is really strong. We illustrate this point in figure 1. In particular, our solver has a sharp decrease at the beginning. This is important for the use inside of a branch-and-bound. Note that even if the total computing time of SB is generally greater than the one of CSDP, the strong tailing-off effect of this solver can be an advantage (the two curves cross each other).

Our solver to compute the bound (4) combines several advantages of the SDP solvers SB and CSDP: it provides SDP-quality bounds, has a strong tailing-off effect, and it is significantly faster (even when the solving process is interrupted). These nice features explain why [START_REF] Malick | Solving k-cluster problems to optimality using adjustable semidefinite programming bounds[END_REF] used it successfully in a branch-and-bound to solve the k-cluster problem exactly. It is also interesting to note that the behavior of the solvers depends a lot on the formulation of the bound, and that dual solvers (like SB and SDLS) are more efficient in our case when there are more dual variables.

Fig. 1 .

 1 Fig. 1. Semidefinite bounds in dependence of time. n = 80, d = 0.5, and k = 40

Table 1

 1 For each n, 45 problems are tested. The averaged relative difference between the computed bounds SDLS and SDP is gap(%).

	Problem	SDLS Θ P (α)	(2) with SB	(2) with CSDP
	n	time (s)	gap(%)	time (s)	time (s)
	80	0.15"	0.07%	1.09"	1.41"
	100	0.19"	0.08%	2.90"	6.70"
	300	2.59"	0.05%	25.84"	186.95"
		SDLS Θ S (α)	(3) with SB	(3) with CSDP
	n	time (s)	gap(%)	time (s)	time (s)
	80	0,66"	1,46%	3.15"	0.34"
	100	0,92"	1,66%	3.80"	0.58"
	300	10,63"	2,11%	39.64"	10.12"

Table 2

 2 Five problems are tested for each (n, k, d). d is the graph density in percent.

		Problem	SDLS	SDP with SB	SDP with CSDP
	n	k	d(%)	time	gap(%)	time	time (s) to	time	time (s) to
				(s)		(s)	achieve SDLS	(s)	achieve SDLS
	80	20	25	0.18"	0.21%	0.55"	0.23"	0.29"	0.19"
			50	0.18"	0.14%	0.87"	0.41"	0.45"	0.35"
			75	0.16"	0.12%	1.37"	0.61"	0.31"	0.24"
		40	25	0.17"	0.06%	0.54"	0.37"	0.35"	0.28"
			50	0.10"	0.04%	0.98"	0.80"	0.33"	0.28"
			75	0.07"	0.03%	1.39"	0.81"	0.40"	0.32"
		60	25	0.15"	0.02%	1.17"	0.85"	0.35"	0.29"
			50	0.14"	0.01%	1.34"	1.30"	0.35"	0.29"
			75	0.15"	0.01%	1.64"	1.41"	0.33"	0.29"
			mean 0.15"	0.07%	1.09"	0.76"	0.34"	0.28"
	100	25	25	0.19"	0.23%	1.27"	0.51"	0.64"	0.50"
			50	0.23"	0.15%	2.40"	0.86"	0.53"	0.42"
			75	0.29"	0.13%	2.54"	1.14"	0.56"	0.45"
		50	25	0.23"	0.07%	1.15"	0.67"	0.52"	0.43"
			50	0.12"	0.05%	2.77"	1.01"	0.57"	0.47"
			75	0.12"	0.05%	2.02"	1.10"	0.59"	0.48"
		75	25	0.17"	0.02%	3.12"	1.75"	0.57"	0.47"
			50	0.17"	0.01%	1.86"	1.37"	0.65"	0.55"
			75	0.16"	0.01%	8.99"	6.25"	0.54"	0.47"
			mean 0.19"	0.08%	2.52"	1.63"	0.58"	0.47"
	300	75	25	3.51"	0.15%	17.27"	7.20"	15.26"	12.40"
			50	3.56"	0.09%	26.96"	8.21"	7.04"	5.38"
			75	3.79"	0.08%	20.24"	9.12"	6.89"	5.60"
		150	25	1.66"	0.05%	11,45"	8.33"	16.25"	12.64"
			50	0.92"	0.02%	13.78"	11.93"	12.04"	9.92"
			75	0.74"	0.04%	8.21"	5.95"	7.30"	6.01"
		225	25	2.68"	0.02%	10.99"	10.05"	8.43"	6.74"
			50	3.01"	0.01%	52.92"	12.85"	7.84"	6.53"
			75	3.45"	0.01%	38.14"	25.66"	10.06"	7.19"
			mean 2.59"	0.05%	22.22"	9.67"	10.12"	8.05"

1 This work has been supported by CNRS through "GdR Recherche Opérationnelle" and by Grenoble University through "Pôle Math-STIC". We thank Quentin Monnet and Lise-Marie Veillon for their valuable help in developing parts of the solver.


Vincent Dubut
email: vincent.dubut@univ-provence.fr

Rémi Grenier

Emese Meglécz

Rémi Chappaz

Caroline Costedoat

Delphine Danancher

Stéphane Descloux

Thibaut Malausa

Jean-François Martin

Nicolas Pech

Jean-François Martin

André Gilles

: E Meglécz

: A Gilles

J.-F Martin

Perciformes: Percidae) and cross-species amplification in five other percids

Keywords: Percidae, STRs, Enriched library, Pyrosequencing, QDD, Conservation genetics

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

The Rhone streber, Zingel asper (Linnaeus, 1758) [Actinopterygii: Perciformes: Percidae], is defined as a critically endangered species by the International Union for Conservation of Nature. Since the beginning of the twentieth century, Z. asper declined by about 80% of its initial distribution range as a result of combined anthropogenic factors: habitat fragmentation, hydraulic disturbance and pollution. This species is now restricted to a few disconnected populations while it was formerly present in the whole Rhone River drainage [START_REF] Mari | A conservation strategy for Zingel asper, a threatened endemic percid of the Rhône basin[END_REF]. Whether this geographic shrinkage and habitat fragmentations caused a loss of genetic diversity and genetic drift within isolated populations remains unclear (but see [START_REF] Laroche | Genetic structure of fragmented populations of a threatened endemic percid of the Rhône River: Zingel asper[END_REF]. Since conservation strategies rely on the understanding of population structure and dynamics, analysing the genetic structure of the Rhone streber with fine-scale molecular markers such as microsatellites would provide useful information. The development of microsatellite markers would allow for delineating the evolutionary history of the species and evaluating the genetic diversity in both evolutionary and conservation frameworks (as initiated in [START_REF] Danancher | Microsatellite analysis of relatedness structure in young of the year of the endangered Zingel asper (Percidae) and implications for conservation[END_REF].

In this study, we detail the development of 58 polymorphic microsatellites for the Rhone streber (including 55 novel markers with perfect motif) using a combination of biotin-enrichment and 454 pyrosequencing technology. We also test for the applicability of these markers on five other percid species (Sander lucioperca, Perca fluviatilis, Gymnocephalus schraetzer, Romanichthys valsanicola and Zingel streber).

Material and methods

Caudal fin samples were collected from 68 individuals of Z. asper in Durance River (southeastern France; n=36) and Beaume River (southeastern France; n=32). Additionally, individuals from five other Percidae species were collected: 30 S. lucioperca individuals (Rhone delta, southeastern France), 33 P. fluviatilis (Durance catchment), one R. valsanicola (Valsan River, Romania), one Z. streber and four G. schraetzer (Danube drainage).

Microsatellites were isolated following a biotinenrichment protocol adapted from [START_REF] Kijas | Enrichment of microsatellites from the citrus genome using biotinylated oligonucleotide sequences bound to streptavidin-coated magnetic particles[END_REF] at Genoscreen (Lille, France). DNA was extracted from samples, digested with RsaI (Fermentas) and then ligated to standard oligonucleotide adapters as described elsewhere [START_REF] Dubut | Isolation and characterization of polymorphic microsatellite loci for the dace complex: Leuciscus leuciscus (Teleostei: Cyprinidae)[END_REF]. Three biotin-labelled oligonucleotides-(TG) 10 , (TC) 13 and (AAC) 7 A-were independently hybridised to the ligated DNA at 56°C for 20 min after initial denaturation of the ligation. The enrichment step was completed using Dynabeads (Invitrogen) and standard procedure. The resulting enriched DNA was amplified using standard adapters, with 25 cycles (20 s at 95°C, 20 s at 60°C and 1 min 30 s at 72°C) and a final elongation step of 30 min at 72°C. The amplicons were immediately purified using QIAquick PCR Purification Kit (QIAGEN). Each purified enriched library was used in the GS-FLX 454 Titanium (Roche Applied Science) library preparation following the manufacturer's protocols at Genoscreen (Lille, France).

Emulsion PCR was carried out at a ratio of 1 copy per bead and subsequently disrupted using isopropanol. Beads containing amplified DNA fragments were enriched and recovered for sequencing to finally provide 50,000 to 70,000 enriched beads for each library. The recovered ssDNA beads were packed onto 1/16 region of a 70×75-mm Titanium PicoTiter plate and sequenced with 200 cycles. Sample preparation and analytical processing such as base calling were performed at Genoscreen (Lille, France) using the manufacturer's protocol for Titanium series.

The selection of sequences for primer design was done with the programme QDD [START_REF] Meglécz | QDD: a user-friendly program to select microsatellite markers and design primers from large sequencing projects[END_REF]. Enrichment adaptors were removed from sequences. Sequences longer than 80 bp and containing perfect microsatellite motif of at least five repetitions for any microsatellite motif of 2-6 bp were selected for further analyses. Sequence similarities were computed through an 'all against all' BLAST [START_REF] Altschul | Gapped BLAST and PSI-BLAST: a new generation of protein database search programs[END_REF]e value 1E-40) in which microsatellite motifs were soft masked. Sequences for which pairwise similarity along the flanking regions was over 95% were grouped into contigs, and a 2/3 majority rule consensus sequence was created from each contig. Sequences which had significant BLAST hit to other sequences with an overall similarity among the flanking region bellow 95% were discarded to avoid potential intragenomic multicopy sequences. All unique sequences (with no BLAST hit to any other '454' reads) and consensus sequences were checked for the presence of short repetitions in the flanking regions. PCR primers were designed using QDD with the following criteria: (a) target microsatellite displayed at least five repetitions, (b) length of PCR product was between 100 and 300 bp, (c) flanking regions contained at most one mononucleotide stretch of four bases and two repetitions of any di-hexa base pair motifs and (d) annealing temperatures of primers pairs were optimised for falling between 57°C and 63°C. Among the 241 sequences for which primer pairs could be designed, we selected 181 sequences by discarding 60 sequences with only five or six repeats.

Amplifications were performed for each of the 181 primers pairs using reagents and protocols described previously [START_REF] Dubut | Isolation and characterization of polymorphic microsatellite loci for the dace complex: Leuciscus leuciscus (Teleostei: Cyprinidae)[END_REF]). The 105 novel primer pairs displaying specific PCR products on agarose gel electrophoresis were retained for further analyses. At this stage, primers pairs of three previously described microsatellite loci (Svi18: [START_REF] Borer | Microsatellites in walleye Stizostedion vitreum[END_REF]SviL8 and SviL9: Wirth et al. 1999) were included into the protocol. All the 108 loci were then amplified separately using forward primers labelled with fluorescent dyes 6-FAM (Eurogentec), PET, NED or VIC (Applied Biosystems). Visualisation of the amplicons was conducted on an ABI 3130XL Genetic Analyzer (Applied Biosystems). Alleles sizes were scored against the GeneScan-500 LIZ® Size Standard (Applied Biosystems), and genotypes were obtained using GeneMapper® 3.7 (Applied Biosystems). A total of 58 primers pairs associated with an unambiguous genotype profile in Z. asper were selected and combined into eight PCR multiplex kits (Table 1). The multiplex kits were further tested on six percid species (including Z. asper).

GENEPOP 4.0 [START_REF] Rousset | GENEPOP'007: a complete reimplementation of the Genepop software for Windows and Linux[END_REF] was used to (a) test Hardy-Weinberg (HW) equilibrium, (b) estimate the heterozygosity for all loci and populations and (c) test for linkage disequilibrium (LD) among loci within populations.

Results

Concerning Z. asper, the number of alleles per locus ranged from 2 to 25, and expected heterozygosity values ranged from 0.03 to 0.95 (Table 2). After FDR correction (modified as proposed by [START_REF] Benjamini | Controlling the false discovery rate-a practical and powerful approach to multiple testing[END_REF], all but two loci (SviL8 and Za038 in the Durance sample, with significant excess of homozygotes) displayed HW equilibrium (Table 2), and significant LD among loci was detected between two loci (Za207 and Za239) in the Beaume sample only.

As for S. lucioperca and P. fluviatilis, the amplification revealed successful for 47 and 35 loci, respectively (Table 2). Eighteen loci were polymorphic in S. lucioperca (although polymorphic, SviL9 was disregarded as homozygotes with null allele were detected), and 14 were polymorphic in P. fluviatilis. All polymorphic loci did not depart from HW equilibrium (except Za072 in S. lucioperca), and no LD was detected, except for one pair of loci (Za121 vs. Za144 in S. lucioperca). In S. lucioperca, the number of alleles per polymorphic locus ranged from 2 to 9, and expected heterozygosity values ranged from 0.32 to 0.85. In P. fluviatilis, the number of alleles per polymorphic locus ranged from 2 to 14, and expected heterozygosity values ranged from 0.06 to 0.88 (Table 2). Additionally, cross-species amplifications were successful in 53.4% cases for G. schraetzer, in 69.0% cases for R. valsanicola and in 70.7% cases for Z. streber (Table 2).

Discussion

The combination of biotin-enrichment protocol and next generation pyrosequencing, through 454 GS-FLX Titanium technology allowed the fast development of polymorphic markers for the threatened Z. asper. The 55 new markers developed here (along with three previously published markers) constitute a useful tool for achieving detailed information on the genetic structure of this species and investigating its evolutionary history. Furthermore, it will assist in the development of comprehensive long-term management and protection plans. In this context, normalisation of the PCR conditions and multiplexing make faster and cost effective the genotyping of the 58 loci. Moreover, all the novel 55 markers display perfect microsatellite motif. This makes them easily usable not only for frequency based analyses but also in a coalescent theory framework for demographic inference. Furthermore, to our knowledge, only a limited number of microsatellites (<15 markers) were developed for S. lucioperca [START_REF] Kohlman | Isolation and characterization of nine microsatellite loci from the pike-perch, Sander lucioperca (Linnaeus, 1758)[END_REF] and P. fluviatilis [START_REF] Yang | Isolation and characterization of 12 polymorphic microsatellite loci in Eurasian perch (Perca fluviatilus L.)[END_REF] to date. This set of microsatellites therefore provides an additional tool to researchers and practitioners in need of genetic markers for investigating the genetic structure and evolutionary history of wild populations and for fishery and aquaculture management. Finally, the success in cross-species amplification for the other percid species highlights the potential usefulness of the developed markers for a broader range of evolutionary, conservation and management studies in percids.

Table 1

 1 Microsatellite loci and multiplex PCR conditions

	Locus	Fluorescent	Primer sequence (5′-3′)	Core motif	Multiplex PCR
	(GenBank ID)	dye
					Kit	Primers
						concentration
						(nM)
	Svi18 (G36964)	NED	F: GATCTGTAAACTCCAGCGTG	(AC) 11	1	400
			R: CTTAAGCTGCTCAGCATCCAGG
	SviL8 (AF144741)	VIC	F: GCTTATACGTCGTTCTTATG	(TG) 22	1	150
			R: ATGGAGAAGCAAGTTGAG
	SviL9 (AF144742)	6-FAM	F: TACTGTTCACTTATCTATCC	(CA) 18 AA(CA) 3 A(AC) 4	1	400
			R: TGTATGTGTGTGTGTTCATGT
	Za022 (HM622293)	VIC	F: AGTCGATGCTTGATGCTGTG	(CT) 10	1	5 0
			R: GAGAAGGAAGGAGAAAGAGATGA
	Za097 (HM622309)	PET	F: TATTGAACGAAGAGCTGCGG	(GA) 8	1	100
			R: TCGGGTTATTTAACCGGCTC
	Za118 (HM622315)	NED	F: TTGACACATATCATGAACCACTACA	(GTT) 9	1	5 0
			R: GACCGCTGGTTCTTCTTCAG
	Za190 (HM622332)	PET	F: CATTCCATTTTGTTGGCTCA	(TGA) 10	1	100
			R: GCTTTGGAAAGAAACGATGG
	Za215 (HM622339)	6-FAM	F: ACGCATCTGTCCACCACTG	(AC) 7	1	100
			R: CCTCCCCACTGGATAGGAAT
	Za143 (HM622318)	VIC	F: TAAGTGTTAAAATTGTAGCCCATTTC	(CA) 12	2	250
			R: GGACATTTCTAAGTGACCCCA
	Za176 (HM622327)	PET	F: AGTGGTTACTTGTGGAGCGG	(CTT) 8	2	250
			R: GATTTCCGATTACCTGGAACA
	Za179 (HM622329)	NED	F: ATTTCCCATTGCGGGATTA	(TCT) 9	2	5 0
			R: GGATTCTTGCATGCTTTGGT
	Za186 (HM622331)	6-FAM	F: CAGCTTGCAGTGAAAGACCA	(AC) 10	2	250
			R: TGGAGAGTGGATGGATTTACAG
	Za207 (HM622337)	VIC	F: GGATTCCAGAAGCAAAAGAGG	(GT) 13	2	100
			R: TGGGACAAGGCTTTAACCAC
	Za209 (HM622338)	NED	F: CATCACTGTCCAGCAAAAGC	(GAA) 8	2	4 0
			R: ATGTGAATTTCCCTTGTGGG
	Za222 (HM622341)	NED	F: TCTCTGTCACAGGAGGTCCA	(AAGGAG) 5	2	7 5
			R: ATTACCGGTTTGAAGGGCTG
	Za040 (HM622299)	PET	F: TTTGTGATATTCCACTCTGAAAGC	(CT) 11	3	100
			R: TGGCATAATTCAGCCATCAA
	Za107 (HM622313)	NED	F: CCTGTACACGTTATTCCCCA	(GA) 17	3	100
			R: CTTCAGCACTGTATTGAGGGG
	Za113 (HM622314)	PET	F: ACCACGCACAATCACTCGTA	(CA) 11	3	100
			R: CCTGGCTTTACCCAGAAACA
	Za144 (HM622319)	NED	F: GCCCACAATAGCACCGTAAT	(AC) 8	3	5 0
			R: TTTGTGAATGTGAGTGAGAGTCAG
	Za161 (HM622322)	VIC	F: AACCAATTTCAGTCCATTCTTCA	(TG) 9	3	250
			R: CAAAGAAACAAAACAACTGTTCC
	Za164 (HM622323)	6-FAM	F: TTCATGCAAGTTTGCCGTT	(GAA) 5	3	150
			R: TGGTTGCCAATCACTGATCTA
	Za165 (HM622324)	VIC	F: GTTGTCTCAAATCACGCACG	(TCT) 8	3	5 0
			R: TTGTCTGGCAACAATAGGTGA
	Za200 (HM622335)	6-FAM	F: GTTAGCGTGCCAAGTTGATG	(TTC) 11	3	100
			R: GTAGGTGGCGGTATGCATTT
	Za030 (HM622296)	NED	F: TTTGTAGGGTGTTTAAAGTTTGC	(GA) 9	4	5 0
			R: ATTTTCTGCAGTGGTCCCAG
	Za032 (HM622297)	VIC	F: GAGATGACATGCCTTGCCTC	(TG) 10	4	150
			R: CACAAACTAAAGACCAACAAACCA

Table 1

 1

	(continued)
	Locus	Fluorescent	Primer sequence (5′-3′)	Core motif	Multiplex PCR
	(GenBank ID)	dye
					Kit	Primers
						concentration
						(nM)
	Za157 (HM622321)	NED	F: TCAGACTTGGAGAAGGAGGG	(GAA) 7	7	4 0
			R: TCAGTCTCTTTCATCGCTGC
	Za181 (HM622330)	6-FAM	F: TGGACTTTTCACCACAGCAG	(TCA) 6	7	100
			R: GGAGGAGGTGGTCAAAGAGA
	Za203 (HM622336)	PET	F: AGCATCCGGTTCTTTACCTG	(AC) 8	7	100
			R: TGGCTGGTATTGAGTGGTAGG
	Za029 (HM622295)	NED	F: TCCTGCTCTAACAGGTGCAA	(AG) 7	8	7 5
			R: CAGACGCACTGATCACCAAT
	Za077 (HM622302)	6-FAM	F: TCTAGCCACCAAATGACCA	(GT) 8	8	500
			R: TTTCACCATCTACGTTTTGTTCTT
	Za178 (HM622328)	PET	F: AGGAGGCAGTAATGCAATGT	(AAG) 7	8	100
			R: GCACTGATGTAAGTGCAATAGGC
	Za237 (HM622342)	6-FAM	F: ATCTCAAGTCATGGGGCATC	(CA) 10	8	150
			R: GGTCCTCTGGTGCAGCTATAA
	Za239 (HM622343)	NED	F: CGAAGTATATTCAGATAGACCCT	(GA) 10	8	100
			R: CTGTCTTTGTCATAAACTTAACTTACTC

Table 2

 2 Levels of variability and amplifiability of 58 microsatellite loci in Z. asper and five other percid species

	Zingel streber	(n=1)		Total size	range (Na)			196 (1)	127-133 (2)	-	-	118 (1)	117-119 (2)	208-212 (2)	108 (1)	124 (1)	266-282 (2)	-	105 (1)	-	122 (1)	-	101-107 (2)	-	199-205 (2)	-	137-145 (2)	235-242 (2)	-	105 (1)	189 (1)	188 (1)	-	90-102 (2)	201-216 (2)	90 (1)	229 (1)
	Romanichthys	valsanicola	(n=1)	Total size	range (Na)			-	-	202 (1)	282 (1)	114 (1)	112 (1)	200 (1)	103 (1)	127 (1)	268 (1)	-	-	121 (1)	122 (1)	105-107 (2)	101-105 (2)	-	-	-	143 (1)	251-267 (2)	109 (1)	99 (1)	192 (1)	188 (1)	132 (1)	96 (1)	151-194 (2)	90-93 (2)	266 (1)	-
	Gymnocephalus	schraetzer	(n=4)	Total size	range (Na)			-	-	236-263 (2)	274 (1)	-	121-130 (5)	219 (1)	129-191 (6)	-	-	-	-	103 (1)	124 (1)	-	-	-	-	167-184 (5)	146 (1)	236-268 (6)	-	-	190-192 (2)	181 (1)	142 (1)	-	-	89 (1)	245-308 (6)	-
				H o /H e				0.55/0.54	nd	0.55/0.74	nd	0.18/0.22	nd	nd	0.21/0.28	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	nd	0.88/0.88	nd	nd	0.67/0.69	nd	0.64/0.58	nd
	Perca fluviatilis	(n=33)		Total size	range (Na)			164-172 (4)	-	198-207 (6)	281 (1)	126-143 (4)	113 (1)	227 (1)	120-124 (2)	126 (1)	-	-	-	-	122 (1)	-	-	114 (1)	-	-	143 (1)	-	112 (1)	-	-	149-194 (14)	-	-	135-155 (4)	86 (1)	228-240 (4)	-
				H o /H e				0.73/0.79	nd	nd	nd	nd	nd	nd	0.47/0.43	nd	nd	nd	0.80/0.77	0.53/0.50	nd	0.23/0.60 a	nd	nd	nd	nd	nd	0.57/0.50	nd	nd	nd	nd	nd	nd	0.70/0.85	nd	0.63/0.60	0.27/0.43
	Sander lucioperca	(n=30)		Total size	range (Na)			153-182 (9)	-	221-224 (3) c	280 (1)	-	121 (1)	202 (1)	127-139 (4)	124 (1)	267 (1)	-	107-130 (6)	111-113 (2)	122 (1)	99-106 (3)	107 (1) c	108 (1)	198 (1)	-	143 (1)	203-207 (2)	106 (1)	-	-	188 (1)	141 (1)	-	169-229 (9)	90 (1)	227-235 (4)	135-148 (5)
				H o /H e	Beaume	River	(n=32)	0.88/0.86	0.25/0.31	0.84/0.79	0.06/0.06	0.38/0.35	0.59/0.48	0.41/0.33	0.22/0.20	0.28/0.32	0.53/0.49	0.75/0.75	0.66/0.50	0.34/0.33	nd b	nd b	0.56/0.51	0.44/0.35	0.78/0.77	0.19/0.18	0.06/0.06	0.75/0.77	0.25/0.37	0.44/0.46	0.75/0.73	0.13/0.12	0.44/0.46	0.47/0.47	0.34/0.34	0.25/0.26	0.69/0.63	0.59/0.50
				H o /H e	Durance	River	(n=36)	0.94/0.95	0.58/0.84 a	0.75/0.88	0.06/0.05	0.06/0.05	0.53/0.59	0.50/0.53	0.06/0.05	0.53/0.45	0.42/0.53	0.33/0.35	0.50/0.69 a	0.39/0.44	0.08/0.08	0.22/0.20	0.28/0.29	0.36/0.40	0.92/0.87	0.06/0.05	0.31/0.30	0.92/0.91	0.33/0.45	0.25/0.22	0.67/0.74	nd b	0.39/0.46	0.61/0.72	0.50/0.56	0.39/0.41	0.64/0.65	0.56/0.74
	Zingel asper			Total size	range (Na)			172-237 (25)	118-156 (12)	202-228 (15)	282-284 (2)	116-118 (2)	117-121 (3)	206-210 (3)	104-106 (2)	122-130 (4)	235-268 (5)	105-167 (14)	105-110 (4)	103-111 (5)	126-128 (2)	98-100 (2)	101-116 (5)	107-111 (3)	143-187 (15)	124-128 (3)	137-143 (2)	203-262 (16)	111-168 (13)	99-103 (3)	180-197 (6)	188-196 (2)	131-143 (4)	92-110 (6)	153-171 (5)	80-96 (4)	205-265 (9)	111-120 (6)
	Locus							Svi18	SviL8	SviL9	Za006	Za011	Za019	Za022	Za024	Za029	Za030	Za032	Za038	Za040	Za069	Za072	Za077	Za078	Za086	Za087	Za091	Za094	Za096	Za097	Za101	Za102	Za106	Za107	Za113	Za118	Za121	Za138

(1)

Acknowledgements We are very grateful to A. Crivelli for the S. lucioperca samples; to E. Corse, G. Olivari and M. Niveau for the P. fluviatilis samples; and to J. Bohlen for the Z. streber and G. schraetzer samples. We are also indebted to the ONEMA Alpes de Haute-Provence for their technical help and assistance in the field. Data used in this work were partly produced using the molecular genetic analysis technical facilities of the IFR119 'Montpellier Environnement Biodiversité'. This work was supported by Electricité de France.

Ethical standards

The authors declare that the experiments described in this paper comply with the current French laws.