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The management strategy for the Antarctic krill (Euphausia superba) fishery is being revised. A key aim is to spatially and temporally allocate catches in a manner that minimizes impacts to both the krill stock and dependent predators. This process requires spatial information on the distribution and abundance of krill, yet gaps exist for an important fishing area surrounding the South Orkney Islands in the south Scotia Sea. To fill this need, we create a dynamic distribution model for krill in this region. We used data from a spatially and temporally consistent acoustic survey (2011-2020) and year-specific environmental covariates within a two-part hurdle model. The model successfully captured observed spatial and temporal patterns in krill density. The covariates found to be most important included distance from shelf break, distance from summer sea ice extent, and salinity. The northern and eastern shelf edges of the South Orkney Islands were areas of consistently high krill density and displayed strong spatial overlap between intense fishing activity and foraging chinstrap penguins. High mean krill density was also linked to oceanographic features located within the Weddell Sea. Our data suggest that years in which these features were closer to the South Orkney shelf were also years of positive Southern Annular Mode and higher observed krill densities. Our findings highlight existing fishery?predator?prey overlap in the region and support the hypothesis that Weddell Sea oceanography may play a role in transporting krill into this region. These results will feed into the next phase of krill fisheries management assessment.

Massive injection of 13C depleted carbon to the ocean and atmosphere coincided with major environmental upheaval multiple times in the geological record. For several events, the source of carbon has been attributed to explosive venting of gas produced when magmatic sills intruded organic-rich sediment. The concept mostly derives from studies of a few ancient sedimentary basins with numerous hydrothermal vent complexes (HTVCs) where craters appear to have formed across large areas of the seafloor at the same time, but good examples remain rare in strata younger than the Early Eocene. We present geophysical data documenting at least 150 large (km-scale) craters on the modern seafloor across ∼148,000 km2 of Scan Basin in the southern Scotia Sea, a remote region offshore Antarctica. Seismic and bathymetric information reveals the craters relate to vertical fluid pipes extending above dome-shaped forced folds and saucer-shaped igneous sills. Presumably, magmatic intrusions deform overlying sediment and produce thermogenic gas, where buoyant hydrothermal fluids migrate upwards from sill flanks through V-shaped gas chimneys to the seafloor. Fluid expulsion, driven by excess pore pressure, enhances vertical conduits and creates collapse structures on the seafloor. Age estimates for sill emplacement and crater formation come from correlations of seismic reflectors with bore hole data collected on IODP Expedition 382. Sills intruded into sediment at least two times, first about 12–13 Ma (Middle Miocene), which occurred with deep intrusions of stacked composite sills, and once about 0.9 Ma and associated with volcanism along Discovery Bank, which may have reactivated previous fluid venting. Crater reactivation has occurred since 0.9 Ma, although probably episodically. Importantly, at present-day, numerous craters related to sills and fluid pipes populate the seafloor above a young sedimentary basin, and the ocean and atmosphere are receiving massive quantities of 13C depleted carbon. The two phenomena are unrelated but, with changes in global climate and sedimentation, the craters could be filled simultaneously and give an impression in the rock record of rapid and coeval formation coincident with carbon emission. Interpretations of ancient HTVCs and their significance to global carbon cycling needs revision with consideration of modern seafloor regions with HTVCs, notably Scan Basin.

We studied the relationship between the proximity of land and the distribution and swarming characteristics of Antarctic krill across the Scotia Sea in January and February 2003. Krill swarms identified with a Simrad EK60 (38 kHz, 120 kHz) echosounder were grouped into 4 categories according to distance from shoreline: 0 to 50 km, 50 to 100 km, 100 to 200 km and >200 km. Cross-sectional areas of swarms were significantly larger inshore, with a mean value of 120 m² in the 0 to 50 km zone compared to <80 m² further offshore. The packing concentration of krill within inshore swarms was also significantly greater, with an average density of 95 ind. m^–3 compared to between 24 and 31 ind. m^–3 elsewhere. A large proportion of the biomass was concentrated into a small number of large, dense swarms throughout the survey area, and this trend increased with decreasing distance from shore. The highest median number of swarms per km and krill acoustic biomass per km was found in the 50 to 100 km zone. However, a significantly greater number of large, biomass-rich swarms occurred in the 0 to 50 km zone compared to all other zones. Swarms in the 0 to 50 km zone were also significantly further apart. The majority of swarms were located in the upper 50 m during the daytime although they were marginally deeper in the night in offshore regions. Krill are likely to move between inshore and offshore environments continuously over their lifetimes. The change in krill behaviour between environments could be a response to local predatory threats over short spatial and temporal scales.

Swarming is a fundamental part of the life of Euphausia superba, yet we still know very little about what drives the considerable variability in swarm shape, size and biomass. We examined swarms across the Scotia Sea in January and February 2003 using a Simrad EK60 (38 and 120kHz) echosounder, concurrent with net sampling. The acoustic data were analysed through applying a swarm-identification algorithm and then filtering out all non-krill targets. The area, length, height, depth, packing-concentration and inter-swarm distance of 4525 swarms was derived by this method. Hierarchical clustering revealed 2 principal swarm types, which differed in both their dimensions and packing-concentrations. Type 1 swarms were generally small (<50m long) and were not very tightly packed (<10ind.m−3), whereas type 2 swarms were an order of magnitude larger and had packing concentrations up to 10 times greater. Further sub-divisions of these types identified small and standard swarms within the type 1 group and large and superswarms within the type 2 group. A minor group (swarm type 3) was also found, containing swarms that were isolated (>100km away from the next swarm). The distribution of swarm types over the survey grid was examined with respect to a number of potential explanatory variables describing both the environment and the internal-state of krill (namely maturity, body length, body condition). Most variables were spatially averaged over scales of ∼100km and so mainly had a mesoscale perspective. The exception was the level of light (photosynthetically active radiation (PAR)) for which measurements were specific to each swarm. A binary logistic model was constructed from four variables found to have significant explanatory power (P<0.05): surface fluorescence, PAR, krill maturity and krill body length. Larger (type 2) swarms were more commonly found during nighttime or when it was overcast during the day, when surface fluorescence was low, and when the krill were small and immature. A strong pattern of diel vertical migration was not observed although the larger and denser swarms tended to occur more often at night than during the day. The vast majority of krill were contained within a minor fraction of the total number of swarms. These krill-rich swarms were more common in areas dominated by small and immature krill. We propose that, at the mesoscale level, the structure of swarms switches from being predominantly large and tightly packed to smaller and more diffuse as krill grow and mature. This pattern is further modulated according to feeding conditions and then level of light.

Identification of DNA sequence diversity is a powerful means for assessing the species present in environmental samples. The most common molecular strategies for estimating taxonomic composition depend upon PCR with universal primers that amplify an orthologous DNA region from a range of species. The diversity of sequences within a sample that can be detected by universal primers is often compromised by high concentrations of some DNA templates. If the DNA within the sample contains a small number of sequences in relatively high concentrations, then less concentrated sequences are often not amplified because the PCR favours the dominant DNA types. This is a particular problem in molecular diet studies, where predator DNA is often present in great excess of food-derived DNA.

A mathematical model describing the development of phytoplankton blooms as a function of the depth of the wind-mixed layer, spectral distribution of light, passage of atmospheric low-pressure systems, size of the initial phytoplankton stock and loss rates is presented. Model runs represent shade-adapted, large-celled, bloom-forming diatoms. Periodic deep mixing caused by strong winds may severely retard the development of blooms and frequently abort them before macronutrients are completely exhausted. Moderate depths of mixing (40-50 m) in combination with a moderately large total loss rate (about 0.01 3 h-1) can prevent blooms from developing during the brightest time of the year. Complete exhaustion of macronutrients in the upper waters is likely only if the wind-mixed layer is less than 10 m deep, i.e. in very sheltered waters, and also in the marginal ice zone when ice is melting. We do not exclude the possibility of control of phytoplankton biomass by iron in ice-free, deep-sea parts of the Antarctic Ocean, but the implied enhancement of export production through addition of iron might be restricted because of limitation by light, i.e. vertical mixing.