Modeling Studies of Antarctic Krill (<i>Euphausia superba</i>) Survival During Transport Across the Scotia Sea and Environs
Notice bibliographique
Résumé
The Antarctic krill (Euphausia superba) populations at South Georgia, which is in the eastern Scotia Sea, are hypothesized to be sustained by import of individuals from upstream regions, such as the western Antarctic Peninsula. To test this hypothesis a modeling framework consisting of the Harvard Ocean Prediction System (HOPS) and a time-dependent, size-structured, physiologically-based krill growth model was developed. The simulated circulation fields obtained from HOPS were used with drifter studies to determine regions and pathways that allow transport of Antarctic krill to South Georgia. Pelagic phytoplankton concentrations along the simulated drifter trajectories were extracted from historical Coastal Zone Color Scanner measurements and sea ice algae concentrations were calculated from sea ice concentration and extent extracted along particle trajectories from Special Sensor Microwave/Imager measurements. As additional food sources, a time series of heterotrophic food was constructed from historical data, and time series of detritus concentrations along simulated drifter trajectories were calculated using phytoplankton concentrations extracted from Coastal Zone Color Scanner measurements together with measured particulate organic carbon to chlorophyll a ratios. These food resources, along specified drifter trajectories were then input to the krill growth model to determine the size and viability of krill during transport from the source region to South Georgia. The drifter simulations showed that krill spawned along the mid to northern portion of the west Antarctic Peninsula continental shelf, coinciding with known krill spawning areas, can be entrained into the Southern Antarctic Circumpolar Current Front and be transported across the Scotia Sea to South Georgia in 10 months or less. Drifters originating on the continental shelf of the Weddell Sea can reach South Georgia as well; however, transport from this region averages about 20 months. The krill growth model simulations showed that no Single food source, such as pelagic phytoplankton, detritus, sea ice algae, or zooplankton, can support continuous growth of Antarctic krill during the 168 to 225 days needed for transport from the western Antarctic Peninsula to South Georgia. However, combinations of the food sources during the transport time enhanced krill survival, with zooplankton (heterotrophic food) and detritus being particularly important during periods of low pelagic phytoplankton concentrations. The growth model simulations also showed that larval and juvenile krill originating along the western Antarctic Peninsula can grow to the 1+ (14 mm to 36 mm) and 2+ (26 mm to 45 mm) sizes observed at South Georgia during the time needed for transport to this region. The additional transport time needed by krill originating in the Weddell Sea allows retention in a potentially high food environment, provided by sea ice, for almost one year. The krill then complete transport to South Georgia in the following year and larval and juvenile krill grow to 2+ (26 mm to 45 mm) and 3+ (35 mm to 60 mm) sizes during transport.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».