Circumpolar Antarctic krill spawning habitat

• Other Authors: Green, David (hasPrincipalInvestigator), Green, David (pointOfContact), Bestley, Sophie (hasPrincipalInvestigator), Corney, Stuart (hasPrincipalInvestigator), Trebilco, Rowan (coInvestigator), Lehodey, Patrick (coInvestigator), Hindell, Mark, Dr (hasPrincipalInvestigator), Institute for Marine and Antarctic Studies (IMAS), University of Tasmania (UTAS) (hasAssociationWith), Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC) (hasAssociationWith), Collecte Localisation Satellites (CLS), Département Ecosystemes Marins (hasAssociationWith)
• Format: Dataset
• Language: unknown
• Published: University of Tasmania, Australia
• Subjects: biota; climatologyMeteorologyAtmosphere; Southern Ocean; Ecosystem Modelling; Antarctic krill; Euphausia superba; FISHERIES; EARTH SCIENCE; AGRICULTURE; AGRICULTURAL AQUATIC SCIENCES; ECOSYSTEM FUNCTIONS; BIOSPHERE; ECOLOGICAL DYNAMICS; Marine and Estuarine Ecology (incl. Marine Ichthyology); BIOLOGICAL SCIENCES; ECOLOGY; Biological Oceanography; EARTH SCIENCES; OCEANOGRAPHY; Antarctic; Antarc*; Antarctic Krill
• Online Access: https://researchdata.edu.au/circumpolar-antarctic-krill-spawning-habitat/1675962; https://metadata.imas.utas.edu.au:443/geonetwork/srv/en/metadata.show?uuid=c6a1ece6-f697-4c7e-9c68-dd6e4e4c0a3d

Antarctic krill is a key component of Southern Ocean ecosystems and there is significant interest in identifying regions acting as sources for the krill population. We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post-spawning, to predict regions that could support high larval production (spawning habitat). We optimise our model on regional data using a maximum likelihood approach and then generate circumpolar predictions of spawning habitat quality. The uploaded datasets represent model predictions of seasonal circumpolar spawning habitat quality of Antarctic krill as well as composite data of the circumpolar mean annual number of weeks in which modelled spawning habitat quality is higher than the summer 80th percentile. We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post-spawning, to predict regions that could support high larval production (spawning habitat). We optimize our model on regional data using a maximum likelihood approach and then generate circumpolar predictions of spawning habitat quality.