De nombreux prédateurs marins se nourrissent directement des prises des pêcheurs. Ces interactions, définies comme de la déprédation, engendrent des conséquences socio-économiques considérables pour les pêcheurs ainsi que des implications de conservation pour la faune sauvage. D’un côté, la déprédation endommage le matériel et augmente l’effort de pêche pour atteindre les quotas. D’un autre côté, la déprédation augmente le risque de mortalité des prédateurs marins (prise accidentelle ou rétorsion létale par les pécheurs). La pêcherie à la palangre est la plus impactée par la déprédation, principalement par les odontocètes, ce qui incite à trouver des solutions. La majorité des études se concentrant sur la déprédation s’est principalement basée sur des observations en surface, de ce fait la manière dont les prédateurs retirent les poissons sur les lignes reste confuse. Par ailleurs, l’impact de la déprédation sur le comportement des pêcheurs ainsi que les facteurs expliquant leur détectabilité n’ont reçu que peu d’intérêt. L’objectif de cette thèse est donc d’étudier ces problématiques par un suivi acoustique, une utilisation de balises et une approche en écologie comportementale humaine, en se concentrant sur la pêcherie palangrière française ciblant la légine australe (Dissostichus eleginoides) impactée par la déprédation des orques (Orcinus orca) et des cachalots (Physeter macrocephalus). Les capitaines ont été décrits comme recherchant leur ressource selon la théorie de « l’optimal foraging », mais avec des perceptions de la compétition et du succès de pêche qui divergent. Certains capitaines seraient ainsi plus enclins à remonter les palangres au plus proche et à rester sur une zone, même en présence de compétition, augmentant alors le risque d’interaction. L’acoustique des navires a révélé que certaines manoeuvres (marche arrière par exemple) propagent différemment sous l’eau. La manière dont les capitaines manoeuvrent leur palangrier influencerait ainsi leur détectabilité et donc leur risque d’interaction avec les prédateurs. D’autre part, l’utilisation de capteurs sur les palangres et les animaux a révélé que les orques et les cachalots sont capables de déprédater sur les palangres posées sur le fond marin. Ces observations laissent à penser que les odontocètes sont en mesure de localiser l’activité de pêche bien avant la remontée de la ligne, ce qui pourrait être expliqué par une signature acoustique spécifique du déploiement de la ligne. L’ensemble des résultats de cette thèse suggère que la déprédation sur les palangres démersales est très probablement sous-estimée. Cette thèse apporte également des éléments importants pour la lutte contre la déprédation, en montrant la nécessité de protéger les palangres dans l’intégralité du processus de pêche. / Many marine predator species feed on fish caught by fishers directly from the fishing gear. Known as depredation this interaction issue has substantial socio-economic consequences for fishermen and conservation implications for the wildlife. Costs for fishers include damages to the fishing gear and increased fishing effort to complete quotas. For marine predators, depredation increases risks of mortality (lethal retaliation from fishers or bycatch on the gear). Longline fisheries are the most impacted worldwide, primarily by odontocetes (toothed whales) depredation, urging the need for mitigation solutions to be developed. Most of studies assessing depredation have primarily relied on surface observation data, thus the way odontocetes interact with longlines underwater remains unclear. Besides, the way fishermen respond to depredation during fishing operations, or can influence their detectability to odontocetes, have been poorly investigated. This thesis therefore aimed at investigating these aspects through a passive acoustic monitoring, bio-logging and human ecology approaches, focusing on the French Patagonian toothfish (Dissostichus eleginoides) longline fisheries impacted by killer whales (Orcinus orca) and sperm whales (Physeter macrocephalus). Firstly, this thesis reveals that captains behave as optimal foragers but with different personal perception of competition and fishing fulfilment. Some captains would thus be more likely to stay within a patch or to haul closest longline even in presence of competition, suggesting these captains would show higher interaction rates. Additionally, the propagation of vessels’ acoustics varied depending on the type of manoeuvre (e.g. going backward vs. forward). The way captains use their vessels to navigate may therefore influence their detectability and so their depredation level. Secondly, loggers deployed on both the longlines (accelerometers) and odontocetes (GPS-TDR) revealed that killer whales and sperm whales are able to depredate on longlines while soaking on the seafloor. These observations suggest, therefore, that odontocetes can localise fishing activity before the hauling, which could be partially explained by specific acoustic signatures recorded during the setting process. Altogether, the results of the thesis suggest that depredation rates on demersal longlines are most likely underestimated. The thesis also brings some important insights for mitigation measures, suggesting that countermeasures should start from setting to hauling.

The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K]

In conservation ecology, abundance estimates are an important factor from which management decisions are based. Methods to estimate abundance of cetaceans from visual detections are largely developed, whereas parallel methods based on passive acoustic detections are still in their infancy. To estimate the abundance of cetacean species using acoustic detection data, it is first necessary to correctly identify the species that are detected. The current automatic PAMGUARD Whistle Classifier used to automatically identify whistle detection of cetacean species is modified with the objective to facilitate the use of these detections to estimate cetacean abundance. Given the variability of cetacean sounds within and between species, developing an automated species classifier with a 100% correct classification probability for any species is unfeasible. However, through the examples of two case studies it is shown that large and high quality datasets with which to develop these automatic classifiers increase the probability of creating reliable classifiers with low and precise misclassification probability. Given that misclassification is unavoidable, it is necessary to consider the effect of misclassified detections on the number of observed acoustic calls detected and thus on abundance estimates, and to develop robust methods to cope with these misclassifications. Through both heuristic and Bayesian approaches it is demonstrated that if misclassification probabilities are known or estimated precisely, it is possible to estimate the true number of detected calls accurately and precisely. However, misclassification and uncertainty increase the variance of the estimates. If the true numbers of detections from different species are similar, then a small amount of misclassification between species and a small amount of uncertainty in the probabilities of misclassification does not have a detrimental effect on the overall variance and bias of the estimate. However, if there is a difference in the encounter rate between species calls associated with a large amount of uncertainty in the probabilities of misclassification, then the variance of the estimates becomes larger and the bias increases; this in return increases the variance and the bias of the final abundance estimate. This study despite not bringing perfect results highlights for the first time the importance of dealing with the problem of species misclassification for cetacean if acoustic detections are to be used to estimate abundance of cetaceans.

Le milieu marin est insonifié par une grand variété de sources acoustiques, qui peuventêtre monitorées par des enregistreurs acoustiques passifs autonomes. Parmi les sons enregistrés, ontrouve un grand nombre de signaux transitoires (signaux éphémères de durée courte), auxquelsappartiennent notamment les signaux impulsionnels que nous étudions dans cette thèse. Les signauximpulsionnels ont des propriétés spécifiques, telles que leur durée très courte (<1ms), leur faiblenombre d’oscillations, leur forte directivité, qui les rendent difficiles à étudier avec les outils detraitement du signal traditionnels (transformée de Fourier, autocorrélation, etc.).Dans un premier temps, nous nous intéressons à la détection des sources qui émettent des sériesd’impulsions rythmées (dauphins, cachalots, bélugas). Cette détection, s’appuie uniquement surles temps d’arrivée des impulsions reçues, pour effectuer une analyse du rythme au moyen d’uneautocorrélation complexe, et construire une représentation temps-rythme, permettant : i) de détecterles rythmes, ii) de connaître les temps de début et fin des émissions rythmées, iii) de connaître lavaleur du rythme et son évolution.Dans un second temps, nous étudions le potentiel d’une technique appelée analyse par récurrence desphases, pour caractériser les formes d’onde des impulsions. Après avoir présenté le cadre général decette méthode d’analyse, nous l’utilisons dans trois chaînes de traitement répondant à chacune destâches suivantes : i) détection des transitoires, ii) caractérisation et reconnaissance des transitoires,iii) estimation des différences des temps d’arrivée des transitoires sur deux capteurs.Toutes les méthodes développées dans cette étude ont été testées et validées sur des données simuléeset sur des données réelles acquises en mer / The underwater environment is insonified by a wide variety of acoustic sourcesthat can be monitored by autonomous passive acoustic recorders. A large number of the recordedsounds are transient signals (short-finite duration signals), among which the pulse signals that westudy in this thesis. Pulse signals have specific properties, such as a very short duration (<1ms), fewoscillations, a high directivity, which make them difficult to study by classical signal processing tools(Fourier transform, autocorrelation).In the first part of this study, we develop a method to detect sound sources emitting rhythmic pulsetrains (dolphins, sperm whales, beluga whales). This detector uses only the time of arrival of pulses atthe hydrophone to perform a rhythm analysis based on a complex autocorrelation and a time-rhythmrepresentation. This allows : i) to detect rhythmic pulse trains, ii) to know the beginning and endingtimes of pulse trains, iii) to know the value of the rhythm.In the second part of this thesis, we study the potential of a method called Recurrence Plot Analysis tocharacterize waveforms of pulse signals. After a general presentation of this method we develop threesignal processing architectures based on it, to perform the following tasks : i) transient detection, ii)transient characterization and pattern recognition, iii) estimation of time difference of arrival of thetransient on two hydrophones.All the methods developped in this thesis are validated on simulated and real data recorded at sea.

Coral reefs around the world are threatened by a variety of sources, from localized impacts, including overfishing and coastal development, to global temperature increases and ocean acidification. Conserving these marine biodiversity havens requires both global and local action informed by scientific research. In this thesis, I use data collected from the coral reefs around Kiritimati atoll (Republic of Kiribati) in the central equatorial Pacific, first to assess the applicability of two common metrics used in passive underwater acoustic research, and second to examine the effects of a marine heatwave and local human disturbance on an assemblage of corallivorous fish. Using acoustic data recorded in 2017 and 2018 on reefs around Kiritimati, I assess how sound pressure level (SPL) and the acoustic complexity index (ACI) respond to changes in fish sounds in a low frequency band (160 Hz – 1 kHz) and snapping shrimp snaps in a high frequency band (1 kHz – 22 kHz). I found that while SPL was positively correlated with increases in fish sounds and snap density, changes in ACI were dependent upon the settings chosen for its calculation, with the density of snaps negatively correlated with ACI across all settings. These findings provide evidence that despite its quick and prolific adoption, acoustic metrics like ACI should be thoroughly field-tested and standardized before they are applied to new ecosystems like coral reefs. Next, using underwater visual censuses (UVCs) of reef fish assemblages, I quantified how two functional groups of corallivores, obligate and facultative, responded to a mass coral mortality event created by the 2015-2016 El Niño. Declines in abundance of both groups were largely driven by the response of coral-associated damselfishes, Plectroglyphidodon johnstonianus in the obligate group and Plectroglyphidodon dickii in the facultative group, to heat stress and subsequent coral mortality. I also observed a significant decline in the species richness of obligate corallivores, and a continued decline in the abundance of obligate corallivores three years after the mass coral mortality event. Additionally, facultative corallivore abundance increased with disturbance, although the effect was modulated by year, likely due to their more adaptable diets. Corallivore assemblage structure was also influenced by the heat stress event, recovery, and local human disturbance. These results detail how an entire corallivorous assemblage is impacted by a coral mortality event and incidentally provide a timeline for corallivore decline. Together, these results provide information about new ways of monitoring coral reefs, and the ways in which two components of the reef fish community, obligate and facultative corallivores, respond to a mass coral mortality event. / Graduate / 2022-01-15

No description available.

O Monitoramento Acústico Passivo (PAM) submarino refere-se ao uso de sistemas de escuta e gravação subaquática, com o intuito de detectar, monitorar e identificar fontes sonoras através das ondas de pressão que elas produzem. Se diz que é passivo já que tais sistemas unicamente ouvem, sem perturbam o meio ambiente acústico existente, diferentemente de ativos, como os sonares. O PAM submarino tem diversas áreas de aplicação, como em sistemas de vigilância militar, seguridade portuária, monitoramento ambiental, desenvolvimento de índices de densidade populacional de espécies, identificação de espécies, etc. Tecnologia nacional nesta área é praticamente inexistente apesar da sua importância. Neste contexto, o presente trabalho visa contribuir com o desenvolvimento de tecnologia nacional no tema através da concepção, construção e operação de equipamento autônomo de PAM e de métodos de processamento de sinais para detecção automatizada de eventos acústicos submarinos. Foi desenvolvido um equipamento, nomeado OceanPod, que possui características como baixo custo de fabrica¸c~ao, flexibilidade e facilidade de configuração e uso, voltado para a pesquisa científica, industrial e para controle ambiental. Vários protótipos desse equipamento foram construídos e utilizados em missões no mar. Essas jornadas de monitoramento permitiram iniciar a criação de um banco de dados acústico, o qual permitiu fornecer a matéria prima para o teste de detectores de eventos acústicos automatizados e em tempo real. Adicionalmente também é proposto um novo método de detecção-identificação de eventos acústicos, baseado em análise estatística da representação tempo-frequência dos sinais acústicos. Este novo método foi testado na detecção de cetáceos, presentes no banco de dados gerado pelas missões de monitoramento. / Passive Acoustic Monitoring (PAM) refers to the use of systems to listen and record underwater soundscape, in order to detect, track and identify sound sources through the pressure waves that they produce. It is said to be passive as these systems only hear, not put noise in the environment, such as sonars. Underwater PAM has various application areas, such as military surveillance systems, port security, environmental monitoring, development of population density rates of species, species identification, etc. National technology in the field is practically nonexistent despite its importance. In this context, this paper aims to contribute to the national technology development in the field by designing, building, and operating a self-contained PAM equipment, also developing signal-processing methods for automated detection of underwater acoustic events. A device, named \"OceanPod\"which has characteristics such as low manufacturing cost, flexibility and ease of setup and use, intended for scientific, industrial research and environmental control was developed. Several prototypes of the equipment were built and used in several missions at seawaters. These missions monitoring, enabled start creating an acoustic database, which provided the raw material for the automated acoustic events detectors and realtime test. Additionally, it is also proposed a new method of detecting, identifying sound events, based on statistical analysis of the time-frequency representation of the acoustic signals. This new method has been tested in the detection of cetaceans present in the database generated by missions monitoring.

Le trafic maritime est le principal contributeur des bruits sous-marins anthropique : depuis les années 1970, l’augmentation du trafic maritime hauturier a provoqué dans certaines zones une augmentation du bruit ambiant de plus de 10 dB. En réponse à cette préoccupation, la Directive Cadre pour la Stratégie pour le Milieu Marin (DCSMM) recommande un suivi acoustique. Peu d’études s’intéressent à l’activité côtière et aux bruits rayonnés par les petites embarcations ainsi qu’à leurs conséquences sur la faune marine alors que ces environnements côtiers sont les pourvoyeurs de 41.7 % des services écosystémiques produits par les océans.A mi-chemin entre le monde académique et le monde industriel, le travail présenté aux différents questions scientifiques et industrielles sur la thématique du trafic côtier, en termes de l’étude de son influence dans le paysage acoustique et de capacité à détecter et classifier les embarcations côtières.En l’absence d’information sur le trafic maritime côtier, un protocole d’identification visuelle par traitement d’images GoPro® produisant les mêmes données que l’AIS (position, vitesse, taille et type d’embarcation) est proposé et permet la création de carte du trafic maritime sur un disque de 1.6km de rayon. D’un point de vue acoustique, le trafic est caractérisé par deux descripteurs acoustiques, le SPL lié à la distance du bateau le plus proche et l’ANL caractérisant le nombre de bateaux dans un disque de 500 m de rayon. Le suivi spatio-temporel de ces descripteurs permet d’identifier l’impact du trafic maritime dans le paysage acoustique des environnements côtiers. La détection et la classification sont réalisées après caractérisation individuelle du bruit par un ensemble de paramètres acoustiques et par l’utilisation d’algorithmes d’apprentissage supervisé. Un protocole spécifique pour la création de l’arborescence de classification est proposé par comparaison des données acoustiques aux caractéristiques physiques et contextuelle de chaque bateau.Les travaux présentés sont illustrés sur la flottille d’embarcations côtières présente dans la baie de Calvi (Corse) durant la saison estivale. / Marine traffic is the main contributor to anthropogenic underwater noise: since the 1970s, the increase in deep-sea shipping has increased the ambient noise by more than 10 dB in some areas. In response to this concern, the Marine Strategy Framework Directive (MSFD) recommends acoustic monitoring. Few studies are concerned with coastal activity and the noises radiated by small craft while these coastal environments are the purveyors of 41.7% of the ecosystem services produced by the oceans.Between the academic and the industrial world, this PhD was to answer the different scientific and industrial questions on the topic of the coastal traffic in terms of the influence in the soundscape and the detection and classification of the coastal craft.Without information on the coastal maritime traffic, a visual identification protocol is proposed using GoPro® images processing and produced the same data as the AIS (position, speed, size and type of craft); It allows to create maritime traffic maps on a disk of 1.6km radius. The traffic is characterized by two acoustic descriptors: the SPL linked to the distance of the nearest boat and the ANL linked to the number of boats present in a 500 m radius disc. The spatiotemporal monitoring of these descriptors allows to identify the impact on the maritime traffic on the coastal acoustic landscape. The acoustic detection and the classification are performed after individual characterization of the noise by a set of acoustic parameters and using of supervised machine learning algorithm. A specific protocol for the creation of the classification tree is proposed by comparing the acoustic data with the physical and contextual characteristics of each boat.The methods are applied on the flotilla of coastal boats present in the Bay of Calvi (Corsica) during summer.

Les océans subissent des pressions d'origine anthropique particulièrement fortes comme la surpêche, la pollution physico-chimique, et le bruit rayonné par les activités industrielles et militaires. Cette thèse se place dans un contexte de compréhension de l'impact du bruit rayonné dans les océans sur les mammifères marins. L'acoustique passive joue donc un rôle fondamental dans ce problème. Ce travail aborde la tâche de détection de signatures acoustiques de mammifères marins dans le spectrogramme. Cette tâche est difficile pour deux raisons : 1. le bruit océanique a une structure complexe (non-stationnaire, coloré), 2. les signaux de mammifères marins sont inconnus et possèdent eux aussi une structure complexe (non-stationnaires bande étroite et/ou impulsionnels). Le problème doit donc être résolu de manière locale en temps-fréquence, et ne pas faire d'hypothèse a priori sur le signal. Des détecteurs statistiques basés uniquement sur la connaissance des statistiques du bruit dans le spectrogramme existent, mais souffrent deux lacunes : 1. leurs performances en terme de probabilité de fausse alarme/ probabilité de détection se dégradent fortement à faible rapport signal à bruit, et 2. ils ne sont pas capables de séparer les signaux à bande étroite des signaux impulsionnels. Ce travail apporte des pistes de réflexion sur ces problèmes.L'originalité de ce travail de thèse repose dans la formulation d'un test d'hypothèse binaire prenant explicitement en compte l'organisation spatiale des pics temps-fréquence. Nous introduisons une méthode d'Analyse de la Densité des Fausses Alarmes (FADA) qui permet de discriminer les régions temps-fréquence abritant le signal de celles n'abritant que du bruit. Plus précisément,le nombre de fausses alarmes dans une région du plan est d'abord modélisé par une loi binomiale, puis par une loi binomiale corrélée, afin de prendre en considération la redondance du spectrogramme. Le test d'hypothèse binaire est résolu par une approche de Neyman-Pearson. Nous démontrons numériquement la pertinence de cette approche et nous la validons sur données réelles de mammifères marins disposant d'une grande variété de signaux et de conditions de bruit. En particulier, nous illustrons la capacité de FADA à discriminer efficacement le signal du bruit en milieu fortement impulsionnel. / The oceans experience heavy anthropogenic pressure due to overfishing, physico-chemical pollution, and noise radiated by industrial and military activities. This work focuses on the use of passive acoustic monitoring of the oceans, as a tool to understand the impact of radiated noise on marine ecosystems, and particularly on marine mammals. This work tackles the task of detection of acoustical signals of marine mammals using the spectrogram. This task is uneasy for two reasons : 1. the ocean noise structure is complex (non-stationary and colored) and 2. the signals of interest are unknown and also shows a complex structure (non-stationary narrow band and/or impulsive). The problem therefore must be solved locally without making a priori hypothesis on the signal. Statistical detectors only based on the local analysis of the noise spectrogram coefficients are available, making them suitable for this problem. However, these detectors suffer two disadvantages : 1. the trade-offs false alarm probability/ detection probability that are available for low signal tonoise ratio are not satisfactory and 2. the separation between narrow-band and impulsive signals is not possible. This work brings some answers to these problems.The main contribution of this work is to formulate a binary hypothesis test taking explicitly in account the spatial organization of time-frequency peaks. We introduce the False Alarm Density Analysis (FADA) framework that efficiently discriminates time-frequency regions hosting signal from the ones hosting noise only. In particular the number of false alarms in regions of the binary spectrogram is first modeled by a binomial distribution, and then by a correlated binomial distribution to take in account the spectrogram redundancy. The binary hypothesis test is solved using a Neyman-Pearson criterion.We demonstrate the relevance of this approach on simulated data and validate the FADA detector on a wide variety of real signals. In particular we show the capability of the proposed method to efficiently detect signals in highly impulsive environment.

O Monitoramento Acústico Passivo (PAM) submarino refere-se ao uso de sistemas de escuta e gravação subaquática, com o intuito de detectar, monitorar e identificar fontes sonoras através das ondas de pressão que elas produzem. Se diz que é passivo já que tais sistemas unicamente ouvem, sem perturbam o meio ambiente acústico existente, diferentemente de ativos, como os sonares. O PAM submarino tem diversas áreas de aplicação, como em sistemas de vigilância militar, seguridade portuária, monitoramento ambiental, desenvolvimento de índices de densidade populacional de espécies, identificação de espécies, etc. Tecnologia nacional nesta área é praticamente inexistente apesar da sua importância. Neste contexto, o presente trabalho visa contribuir com o desenvolvimento de tecnologia nacional no tema através da concepção, construção e operação de equipamento autônomo de PAM e de métodos de processamento de sinais para detecção automatizada de eventos acústicos submarinos. Foi desenvolvido um equipamento, nomeado OceanPod, que possui características como baixo custo de fabrica¸c~ao, flexibilidade e facilidade de configuração e uso, voltado para a pesquisa científica, industrial e para controle ambiental. Vários protótipos desse equipamento foram construídos e utilizados em missões no mar. Essas jornadas de monitoramento permitiram iniciar a criação de um banco de dados acústico, o qual permitiu fornecer a matéria prima para o teste de detectores de eventos acústicos automatizados e em tempo real. Adicionalmente também é proposto um novo método de detecção-identificação de eventos acústicos, baseado em análise estatística da representação tempo-frequência dos sinais acústicos. Este novo método foi testado na detecção de cetáceos, presentes no banco de dados gerado pelas missões de monitoramento. / Passive Acoustic Monitoring (PAM) refers to the use of systems to listen and record underwater soundscape, in order to detect, track and identify sound sources through the pressure waves that they produce. It is said to be passive as these systems only hear, not put noise in the environment, such as sonars. Underwater PAM has various application areas, such as military surveillance systems, port security, environmental monitoring, development of population density rates of species, species identification, etc. National technology in the field is practically nonexistent despite its importance. In this context, this paper aims to contribute to the national technology development in the field by designing, building, and operating a self-contained PAM equipment, also developing signal-processing methods for automated detection of underwater acoustic events. A device, named \"OceanPod\"which has characteristics such as low manufacturing cost, flexibility and ease of setup and use, intended for scientific, industrial research and environmental control was developed. Several prototypes of the equipment were built and used in several missions at seawaters. These missions monitoring, enabled start creating an acoustic database, which provided the raw material for the automated acoustic events detectors and realtime test. Additionally, it is also proposed a new method of detecting, identifying sound events, based on statistical analysis of the time-frequency representation of the acoustic signals. This new method has been tested in the detection of cetaceans present in the database generated by missions monitoring.