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| Paris, Claire B.; Berenshtein, Igal; Trillo, Marcia L.; Faillettaz, Robin; Olascoaga, Maria J.; Aman, Zachary M.; Schlueter, Micheal; Joye, Samantha B.. |
| After the Deepwater Horizon oil platform explosion, an estimated 172.2 million gallons of gas-saturated oil was discharged uncontrollably into the Gulf of Mexico, causing the largest deep-sea blowout in history. In an attempt to keep the oil submerged, massive quantities of the chemical dispersant Corexit (R) 9500 were deployed 1522 m deep at the gushing riser pipe of the Macondo prospect's wellhead. Understanding the effectiveness of this unprecedented subsea dispersant injection (SSDI) is critical because deepwater drilling is increasing worldwide. Here we use the comprehensive BP Gulf Science Data (GSD) to quantify petroleum dynamics throughout the 87-day long blowout. The spatio-temporal distribution of petroleum hydrocarbons revealed consistent higher... |
| Tipo: Text |
Palavras-chave: Oil spill; Deep-sea blowout; Chemical dispersants; Water column; Macondo; Subsea dispersant injection; Petroleum; Gulf Science Data. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00634/74617/74546.pdf |
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| Van Sebille, Erik; Griffies, Stephen M.; Abernathey, Ryan; Adams, Thomas P.; Berloff, Pavel; Biastoch, Arne; Blanke, Bruno; Chassignet, Eric P.; Cheng, Yu; Cotter, Colin J.; Deleersnijder, Eric; Doos, Kristofer; Drake, Henri F.; Drijfhout, Sybren; Gary, Stefan F.; Heemink, Arnold W.; Kjellsson, Joakim; Koszalka, Inga Monika; Lange, Michael; Lique, Camille; Macgilchrist, Graeme A.; Marsh, Robert; Adame, C. Gabriela Mayorga; Mcadam, Ronan; Nencioli, Francesco; Paris, Claire B.; Piggott, Matthew D.; Polton, Jeff A.; Ruehs, Siren; Shah, Syed H. A. M.; Thomas, Matthew; Wang, Jinbo; Wolfram, Phillip J.; Zanna, Laure; Zika, Jan D.. |
| Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for... |
| Tipo: Text |
Palavras-chave: Ocean circulation; Lagrangian analysis; Connectivity; Particle tracking; Future modelling. |
| Ano: 2018 |
URL: http://archimer.ifremer.fr/doc/00412/52324/53099.pdf |
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| Faillettaz, Robin; Paris, Claire B.; Irisson, Jean-olivier. |
| Most demersal fishes undergo a dispersal phase as larvae, which strongly influences the connectivity among adult populations and, consequently, their genetic structure and replenishment opportunities. Because this phase is difficult to observe directly, it is frequently simulated through numerical models, most of which consider larvae as passive or only vertically migrating. However, in several locations, including the Mediterranean Sea, many species have been shown to swim fast and orient. Here we use a Lagrangian model to study connectivity patterns among three Mediterranean Marine Protected Areas (MPAs) and compare simulations in which virtual larvae are passive to simulations in which oriented swimming is implemented. The parameterization of behavior... |
| Tipo: Text |
Palavras-chave: Fish larvae; Behavior; Swimming; Connectivity; Dispersal; Mediterranean sea; Marine protected areas; Modeling. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00634/74618/74548.pdf |
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| Faillettaz, Robin; Johnson, Eve; Dahlmann, Patrick; Syunkova, Alexandra; Stieglitz, John; Benetti, Daniel; Grosell, Martin; Paris, Claire B.. |
| Understanding the orientation behavior and capabilities in early life history (ELH) of fishes is critical for studying their dispersal but has, surprisingly, never been tested in any pelagic species. We here investigate the ontogeny of orientation and swimming abilities of the pelagic Coryphaena hippurus Linnaeus, 1758 larvae, hereafter mahi-mahi, through their ELH stages using the Drifting In Situ Chamber (DISC) in a laboratory setup. The DISC was deployed in a large (3 m3) circular aquarium in order to control the stimulus perceived by the fish and to identify behavioral response at the individual, developmental stage, and population levels. A total of 79 individual ranging from 7 to 23 days post hatch and from preflexion to early juvenile stages were... |
| Tipo: Text |
Palavras-chave: S: early life history stages; Coryphaena hippurus; Common dolphinfish; Mahi-mahi; Behavior; Orientation; Phototaxism; Ontogeny; Directionality competence. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00654/76610/77760.pdf |
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| Schlenker, Lela S.; Faillettaz, Robin; Stieglitz, John D.; Lam, Chi Hin; Hoenig, Ronald H.; Cox, Georgina K.; Heuer, Rachael M.; Pasparakis, Christina; Benetti, Daniel D.; Paris, Claire B.; Grosell, Martin. |
| Identifying complex behaviors such as spawning and fine-scale activity is extremely challenging in highly migratory fish species and is becoming increasingly critical knowledge for fisheries management in a warming ocean. Habitat use and migratory pathways have been extensively studied in marine animals using pop-up satellite archival tags (PSATs), but high-frequency data collected on the reproductive and swimming behaviors of marine fishes has been limited by the inability to remotely transmit these large datasets. Here, we present the first application of remotely transmitted acceleration data to predict spawning and discover drivers of high activity in a wild and highly migratory pelagic fish, the mahi-mahi (Coryphaena hippurus). Spawning events were... |
| Tipo: Text |
Palavras-chave: Reproductive ecology; Pop-up satellite archival tag (PSAT); Pelagic; Spawning; Migration. |
| Ano: 2021 |
URL: https://archimer.ifremer.fr/doc/00683/79511/82225.pdf |
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| Faillettaz, Robin; Blandin, Agathe; Paris, Claire B.; Koubbi, Philippe; Irisson, Jean-olivier. |
| Mortality is very high during the pelagic larval phase of fishes but the factors that determine recruitment success remain unclear and hard to predict. Because of their bipartite life history, larvae of coastal species have to head back to the shore at the end of their pelagic episode, to settle. These settlement-stage larvae are known to display strong sensory and motile abilities, but most work has been focused on tropical, insular environments and on the influence of coast-related cues on orientation. In this study we quantified the in situ orientation behavior of settlement- stage larvae in a temperate region, with a continuous coast and a dominant along-shore current, and inspected both coast-dependent and independent cues. We tested six species: one... |
| Tipo: Text |
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| Ano: 2015 |
URL: https://archimer.ifremer.fr/doc/00634/74620/74540.pdf |
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| Faillettaz, Robin; Paris, Claire B.; Vaz, Ana C.; Perlin, Natalie; Aman, Zachary M.; Schlüter, Michael; Murawski, Steven A.. |
| The droplet size distribution (DSD) formed by gas-saturated oil jets is one of the most important characteristics of the flow to understand and model the fate of uncontrolled deep-sea oil spills. The shape of the DSD, generally modeled as a theoretical lognormal, Rosin-Rammler or non-fundamental distribution function, defines the size and the mass volume range of the droplets. Yet, the fundamental DSD shape has received much less attention than the volume median size (d50) and range of the DSD during ten years of research following the Deepwater Horizon (DWH) blowout. To better understand the importance of the distribution function of the droplet size we compare the oil rising time, surface oil mass, and sedimented and beached masses for different DSDs... |
| Tipo: Text |
Palavras-chave: Droplet size distribution; DSD; Oil spill; Oil spill model; Oil model; Deepwater Horizon; Blowout; D50; Probability distribution function. |
| Ano: 2021 |
URL: https://archimer.ifremer.fr/doc/00664/77654/79990.pdf |
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