Cécile Vincent

Status of grey seals along mainland Europe from the Southwestern Baltic to France

An air bag inflator (10) includes inflation fluid stored in a chamber (18). The inflator (10) includes a combustible sheet member (100) which comprises an oxidizable substrate, such as a polytetrafluoroethylene film, and a layer of a fuel material, such as magnesium, on the substrate. The combustible sheet member (100) is disposed in a perforated tube (90) in the chamber (18). The combustible sheet member (100), when ignited by an igniter (70), emits combustion products through openings (98) in the perforated tube (90) to warm and increase the pressure of the inflation fluid in the chamber (18). The inflation fluid includes 5% to 20% oxygen to support the combustion of the combustible sheet member (100).

Grey seal diet at the southern limit of its European distribution: combining dietary analyses and fatty acid profiles

The north-east Atlantic grey seal, Halichoerus grypus, is widely distributed along the European coastline from northern Russia to France, with the core population centred around Scotland. To date, very little is known of the diet of the species at the southern margin of the species range. However, because grey seal numbers have been increasing over the last few decades in France, the issue of their potential interactions with coastal fisheries is frequently raised. The diet of grey seal in the Molene Archipelago was investigated by combining scat, stomach content and fatty acid analyses, since all three approaches have complementary potentials to reveal feeding habits of a predator. A total of 145 scats mostly of moulting adult males, 14 stomach contents of yearlings and 14 blubber samples from animals of all ages were analysed following standard methodologies. Scats revealed a diet mainly constituted of 50.6% by mass (M) of wrasse, Labridae (mostly Labrus bergylta), 20.7%M conger eel, Conger conger, and 11.9%M sea bass, Dicentrarchus labrax. Stomach contents were made up of 52.3%M cuttlefish, Sepia officinalis, 9.5%M sole, Solea solea, and 9.4 %M conger eel. All these prey are different from the diet observed in core areas. Fatty acid analysis from the blubber confirmed that the diet differed between the Molene Archipelago and one of the Scottish breeding sites. It also showed that most of the interindividual variability was explained by variation in seal body masses, which could be linked to behavioural ontogeny of foraging strategies. Most of the prey species identified in the food of the grey seal in Brittany are also targeted by professional and/or recreational fisheries in the area; additionally, several prey size-ranges also partly overlap with marketed size-ranges for several species.

Abundance estimate and seasonal patterns of grey seal (Halichoerus grypus) occurrence in Brittany, France, as assessed by photo-identification and capture-mark-recapture

It has been suggested that the large grey seal colonies around the British Isles form local populations within a metapopulation, and that seal movements outside the breeding season lead to considerable overlap between individual home ranges. Individual behaviour and population dynamics of small peripheral colonies may also play a role in the metapopulation. We studied the French grey seal colony of the Molene archipelago, at the southern-most limit of the species’ range. We analysed photo-identification data with capture–mark–recapture techniques in order to estimate the total seasonal abundance of grey seals in the archipelago and to quantify the seasonal rates of occurrence or movements of male and female seals. We found that between 58 (95% confidence interval: 48–71) and 98 (95% CI: 75–175) individuals hauled out in the archipelago during the summers of 1999 and 2000. The use of multistate models allowed the assessment of seasonal site fidelity and indicated that it varied between key periods of the annual cycle, particularly for females. Males showed a constant fidelity rate of 56% from one season to another. Hence, even though they showed high inter-annual site fidelity, they did not seem to have a preferred season for using the archipelago. On the contrary, female grey seals showed the highest site fidelity between moult and summer (around 80%), and the lowest fidelity between summer and the breeding period (34–43%). Thus, females seem to use the Molene archipelago preferentially in summer and leave the site before the breeding season, which explains the very low local pup production. Philopatry may explain this pre-breeding emigration, and we suggest that most grey seals observed in the Molene archipelago were born and breed in other local breeding populations, probably the south-western British Isles.

Modelling Mobile Object Activities Based on Trajectory Ontology Rules Considering Spatial Relationship Rules

Several applications use devices and capture systems to record trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Temporal, spatial and domain related information are fundamental sources used to upgrade trajectories. The objective of semantic trajectories is to help users validating and acquiring more knowledge about mobile objects. In particular, temporal and spatial analysis of semantic trajectories is very important to understand the mobile object behaviour. This article proposes an ontology based modelling approach for semantic trajectories. This approach considers different and independent sources of knowledge represented by domain and spatial ontologies. The domain ontology represents mobile object activities as a set of rules. The spatial ontology represents spatial relationships as a set of rules. To achieve this approach, we need an integration between trajectory and spatial ontologies.

Time Integration in Semantic Trajectories Using an Ontological Modelling Approach

Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easily capture trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research fields are currently focusing on semantic trajectories to support queries and inferences to help users for validating and discovering more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and space knowledge are fundamental sources of information used by the inference operation on semantic trajectories. This article presents a case study of inference mechanism on semantic trajectories. We propose a solution based on an ontological approach for modelling semantic trajectories integrating time information and rules. We give experiments and evaluations of the proposed approach on generated and real data.

Shipping noise in a dynamic sea: a case study of grey seals in the Celtic Sea

Shipping noise is a threat to marine wildlife. Grey seals are benthic foragers, and thus experience acoustic noise throughout the water column, which makes them a good model species for a case study of the potential impacts of shipping noise. We used ship track data from the Celtic Sea, seal track data and a coupled ocean-acoustic modelling system to assess the noise exposure of grey seals along their tracks. It was found that the animals experience step changes in sound levels up to ~20dB at a frequency of 125Hz, and ~10dB on average over 10-1000Hz when they dive through the thermocline, particularly during summer. Our results showed large seasonal differences in the noise level experienced by the seals. These results reveal the actual noise exposure by the animals and could help in marine spatial planning.

Trajectory ontology inference considering domain and temporal dimensions—Application to marine mammals

Abstract Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In our approach, we suggest expressing this knowledge in the form of rules. To annotate data with these rules, we need an inference mechanism over trajectory ontology. Experiments over our model using domain and temporal rules address an inference computation complexity. This complexity has two important factors: time computations and space storage. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. In this paper, we define a refinement specifically for the application domain. Then, we evaluate our contribution over real trajectory data. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. This refinement reduces half of the time computation and then allows considering larger data sets.

Analysis of Trajectory Ontology Inference Complexity over Domain and Temporal Rules

Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The secondary filter then computes the inference over the filtered trajectory data and yields to the final knowledge base, that the user can query.

An Ontology-Based Approach for Handling Explicit and Implicit Knowledge over Trajectories

The current information systems manage several, different and huge databases. The data can be temporal, spatial and other application domains with specific knowledge. For these reasons, new approaches must be designed to fully exploit data expressiveness and heterogeneity taking into account application’s needs. As part of ontology-based information system design, this paper proposes an ontology modeling approach for trajectories of moving objects. Consider domain, temporal and spatial knowledge gives a complexity to our system. We propose optimizations to annotate data with these knowledge.