ANALYSE S1 SMIA PDF

UNIVERSITE HASSAN II DE CASABLANCA. FACULTE DES SCIENCES. AIN CHOCK. ANNEE UNIVERSITAIRE: / SEMESTRE: S1. FILIERE: SMIA . Liste provisoire des inscrits dans la Filière SMIA (semestre S1) A et B VAL I VAL I I I I I VAL Analyse I I I I I I I I I I I I I I I I I I I I I I I I I I I I I VAL I I I I I I I I I I I Algèbre . PLANNING SEANCES D’EXAMEN DE TRAVAUX PRATIQUES. Etudiants SVT- S1. MODULE M2 Histologie– EmbryologieSVT-S1. Etudiants SVT-S1. TP Virtuels .

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Conceived and designed the experiments: We used acoustic telemetry to describe the patterns of vertical movement, site fidelity and residency of grey reef sharks Carcharhinus amblyrhynchos on the outer slope of coral reefs in Palau, Micronesia, over a period of two years and nine months.

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We tagged 39 sharks mostly adult females of which 31 were detected regularly throughout the study. Sharks displayed strong inter-annual residency with greater attendance at monitored sites during summer than winter months.

More individuals were detected during the day than at night. Sharks descended to greater depths and used a wider range of depths around the time of the full moon. There were also crepuscular cycles in mean depth, with sharks moving into shallower waters at dawn and dusk each day.

We suggest that daily, lunar and seasonal cycles in vertical movement and residency are strategies for optimising both energetic budgets and foraging behaviour. Cyclical patterns of movement in response to environmental variables might affect the susceptibility of reef sharks to fishing, a consideration that should be taken into account in the implementation of conservation strategies.

Free-ranging marine predators such as sharks live in a three-dimensional environment where they are able to move in both horizontal and vertical planes. In coral reef ecosystems, most studies of the movement of sharks have focused on defining patterns of use of space on a horizontal plane, many with the ultimate goal of contributing to spatial management strategies, such as marine protected areas, to ensure the adequate conservation of shark populations.

Such studies show that site fidelity is a common phenomenon in many species, including whitetip Triaenodon obesustawny nurse Ginglymostoma cirratumblacktip Carcharhinus melanopterusCaribbean C. The degree of fidelity appears to vary according to life history stage, availability of resources and area of suitable habitat [6][7][8].

Strong site fidelity of juveniles to nursery areas is evident in lemon Negaprion brevirostrisblacktip and Caribbean reef sharks and is thought to be due to the advantages of nurseries in terms of predator avoidance and food availability [9][10][11]. Site fidelity is also common in adult reef sharks, although typically more sporadic when compared to juveniles, which might be partially explained by ontogenetic increases in the size of home ranges [8][12].

Adult site fidelity is argued to be advantageous for a number of reasons, including mating, feeding, pupping and resting [12]. While these studies have contributed to our understanding of the habitat preferences of sharks in reef ecosystems, there is an almost complete lack of equivalent data on the movements of reef sharks in the vertical plane of the water column. In the open ocean, cycles in vertical movement are a fundamental part of the behaviour of predatory species that reflect both changes in physical environments and distributions of prey.

For example, pelagic species including swordfish Xiphias gladiusyellowfin Thunnus albacares and big eye T. In temperate systems, some coastal species, such as the leopard shark Triakis semifasciataalso show daily vertical migrations and actively use shallow, warm waters in the day and late afternoon to increase the core body temperature to optimise rates of digestion, growth and gestation [17].

The limited information that is available suggests that cycles in vertical movement are also a feature of the behaviour of reef sharks.

Whitetip reef sharks do not appear to display diel patterns in depth preferences, but occupy a wider depth range during the night when actively hunting than during the day when resting [4][20]. Together, these studies suggest a range in patterns of vertical movements by sharks in coral reefs that reflect a variety of ecological drivers.

A better understanding of the ecology of reef sharks in coral reef systems requires the examination of movement and residency patterns on both horizontal and vertical planes. Here, we describe spatial and temporal patterns in the vertical movements and residency of the grey reef shark, one of the most common and abundant sharks on coral reefs across the Indo-Pacific.

At our study site in Palau, Micronesia, grey reef sharks tend to form predictable aggregations on outer parts of reef slopes and crests exposed to high current flow.

We used acoustic telemetry to describe patterns of spatial and temporal use of aggregation sites by grey reef sharks over multiple years. A combination of acoustic telemetry and environmental data was also used to test the hypothesis that the vertical movements and residency patterns by grey reef sharks were related to environmental variables, notably water temperature.

Our study contributes to a better understanding of the ecology of these animals and has implications for the management of sharks at aggregation sites, an important driver for diving ecotourism and the Palauan economy [21]. Shark tagging in was also conducted under UWA animal ethics permit no. Our study location was the edge of the main island platform that consists of a large shallow-water lagoon arrayed with small, uplifted limestone islands and a large volcanic island, all of which are enclosed by a km barrier reef [22].

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Grey reef sharks regularly aggregate at sites along the outer reef slope in the southwest leeward quadrant of the barrier reef Figure 1 at promontories where the crenulated reef margin juts out into the flow of the prevailing current [21].

Outer reef slope of the southwest barrier reef of Palau, showing location of receivers. Top left box indicates the study site in the main island platform. We used acoustic receivers VR2w, Vemco to monitor the attendance of tagged sharks at five aggregation sites. We moored receivers at depths between 25 and 40 m on the reef wall or slope and downloaded data from them at one to eight month intervals.

The acoustic array monitored two areas on the barrier reef characterised by vertical walls and steep slopes [22]. The receivers were distributed over a linear distance of approximately 6 km in the northern area and 5 km in the southern area Figure 1. The first receiver was deployed in Novemberwith the remainder deployed between May and July We used hand reels fitted with baited barbless hooks to catch sharks at each of the receiver deployment sites within an area Figure 1Table 1.

Once caught, sharks were brought alongside the boat and restrained within a canvas stretcher, which was then lifted onboard. Sharks were turned upside-down to induce tonic immobility and placed in a holding tank with a constant flow of water into the mouth and through the gills. We recorded the sex, measured the total length L T and surgically implanted an acoustic transmitter into the peritoneal cavity of each shark [23].

This tagging procedure typically required less than ten minutes from the moment the shark was caught to the moment it was released. We classified individuals as sexually mature according to the L T [24]. We used a combination of Vemco VH coded tags power output dB, frequency of 69 Khz with an estimated battery life of 3. Ten of these tags were also fitted with pressure sensors that recorded depths to a maximum of m five VH, deployed inm two VH deployed in or m three VH deployed in L T indicates total length of individual.

Of these, 17 sharks were tagged in the northern area and 22 in the southern area. Two of the tagged sharks were not detected by the array and one individual was detected for only seven days; data for these sharks were not included in analyses.

In Aprilwe conducted range testing of the receivers in the northern site by deploying a test tag VH, power output dB, frequency of 69 Khz, fixed delay and estimating the detection coefficient at intervals of m along transects parallel and perpendicular to the receiver deployment sites. The long-term performance of the receivers was of concern given the large number of tagged individuals in an environment with a complex current regime and reef habitat [22].

In order to assess performance we used metrics developed by Simpfendorfer et al. To estimate levels of biotic and abiotic interference in detection probabilities [26]we deployed a control tag on the reef wall in the southern area for a period of days.

This tag was located m from the receiver at Blue Corner Incoming Figure 1. We used hourly and daily attendance as metrics to describe the general patterns of site fidelity of sharks at deployment sites of receivers.

A shark was considered to be present if two or more detections were recorded in the same day. The use of metrics based on hourly or daily attendance instead of detections reduced the effects of differences in detection probability related to the use of tags with different signal outputs.

To describe site fidelity, we estimated the residency index as the proportion of monitored days during which a shark attended a given smiz. We also estimated the mean number of hours detected per day when a shark attended a given site. We considered sharks as inter-annual residents when an animal had an anwlyse residency index equal or higher than 0. We also calculated the daily attendance index as the longest time series of consecutive days each shark attended a monitored site divided by the total number of days the shark was monitored.

As time series were often interrupted by downloading of receivers, each portion of the interrupted series was considered to be independent and for this reason, the daily attendance index was likely to be a conservative metric of site fidelity at monitored sites.

We quantified differences in site preferences by calculating the standardised daily attendance as the percentage of sharks tagged in each area attending each receiver on each day.

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We used ANOVA and a t-test [27] to compare site preferences in the southern and northern areas respectively. To determine movement between these areas, we estimated the minimum linear dispersal [3]minimum dispersal time as the time between the last detection in the residency area and the time of the first detection in the visiting areaand time spent hours detected in each visiting event.

A shark was considered to be present in the visited area if two or more detections were recorded by the receivers within a period of two hours. To analyse diel patterns in reef attendance we applied a Fast-Fourier transformation [28] to the detection frequency of each shark when the individual had a residency index higher than 0.

The hourly detection frequencies were corrected to account for variations in the detection probability [26].

We analysed the northern and southern areas separately, due to preliminary results suggesting that there was limited movement away from the area in which each animal was tagged. We also calculated mean detection frequency of sharks combined per month in each area and employed circular regression to quantify seasonal patterns in attendance [30].

We corrected the detection frequencies using the correction factors calculated from the data of our control tag [26]. We applied a generalised linear model GLM with bootstrap sampling to examine the effects of environmental factors on the patterns of depth usage of sharks inusing the mean daily depth of all tagged sharks as the response variable. For this model, water temperature and moon phase were used as explanatory variables.

Our temperature dataset consisted of mean weekly water temperature at 57 m depth in the proximity of the monitored sites in both areas source: Coral Reef Research Foundation, Palau. There was little variation in the temperature between the northern and southern areas, thus we combined data from both for subsequent analyses. Accessed March 3. We also used circular regression to identify patterns of depth usage in relation to diel cycles.

As circular regression has low sensitivity to missing data [30]we used the mean depth of the sharks combined per hourly bin over the entire study period for the analysis.

Isabelle BLANC Professeur at MINES ParisTech, PSL Research University

We also used GLMs to establish the relationship between shark attendance and environmental variables within each area. High and low tide phases were defined as x1 hour prior to and following the slack tide [32]. Instantaneous records of shark attendance were aggregated into hourly estimates using a subset function in R [33] that selected values from the data record for each shark.

Due to the autocorrelation inherent in the data, the assumption of temporal independence was violated [34] ; we addressed this analyae by using a matched-block sampling anaylse replacement technique [35][36].

Briefly, this method sub-samples and replaces optimum block lengths from the dataset that maintain some of the autocorrelation structure. Blocks were then joined in a random order to create the uncorrelated bootstrapped smiaa [35][36][37]. Our array of receivers operated continuously during the period of study however, due to technical issues, the receivers from the Blue Corner Incoming and Blue Corner Outgoing sites Figure 1 were not operational from April to November and March to Aprilrespectively Figure S1.

Range testing indicated that there was an overall decrease in the detection coefficient within a m radius of the receivers. Analye receivers with the exception of the receiver at Ulong Sand Bar operated with overall mean code detection efficiency CDE above 0. Following the last deployment of tags in Aprilthere was a considerable decrease in CDE for a number of receivers in x1 the northern and southern areas. A concurrent increase in the rejection coefficient values RC suggests that tag collisions likely contributed anayse the drop in performance of receivers at this time.

The receivers recorded a total of 2. Of the remaining sharks, four were detected daily or weekly for two to 21 months following tagging, although wnalyse this time detections ceased. One adult female that was detected at sites on a weekly basis for 14 months after tagging was then not detected for 12 months, after which time she returned to the receiver array and was detected daily for the following two months until the final data download Table 1.

Overall the residency index among the tagged sharks was 0. Most sharks were detected regularly at sites adjacent smiia where they were tagged Table 1. Movement between the northern and southern areas was low and recorded for only four sharks. Of these, two individuals were recorded twice out of the area where they were tagged, while the remaining two sharks attended their non-residency area only once. The mean minimum linear distance of movements of these animals was

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