CCAMLR

For the first time the entire sequence of the mating behaviour of Antarctic krill (Euphausia superba) in the wild is captured on underwater video. This footage also provides evidence that mating can take place near the seafloor at depths of 400–700 m. This observation challenges the generally accepted concept of the pelagic lifestyle of krill. The mating behaviour observed most closely resembles the mating behaviour reported for a decapod shrimp (Penaeus). The implications of the new observation are also discussed.

Antarctic krill embryos and larvae were experimentally exposed to 380 (control), 1000 and 2000 μatm pCO2 in order to assess the possible impact of ocean acidification on early development of krill. No significant effects were detected on embryonic development or larval behaviour at 1000 μatm pCO2; however, at 2000 μatm pCO2 development was disrupted before gastrulation in 90 per cent of embryos, and no larvae hatched successfully. Our model projections demonstrated that Southern Ocean sea water pCO2 could rise up to 1400 μatm in krill’s depth range under the IPCC IS92a scenario by the year 2100 (atmospheric pCO2 788 matm). These results point out the urgent need for understanding the pCO2-response relationship for krill developmental and later stages, in order to predict the possible fate of this key species in the Southern Ocean.

The driving factors of survival, a key demographic process, have been particularly challenging to study, especially for winter migratory species such as the Adélie penguin (Pygoscelis adeliae). While winter environmental conditions clearly influence Antarctic seabird survival, it has been unclear what environmental features they are most likely to respond to. Here we examine the influence of environmental fluctuations, broad climatic conditions and the success of the breeding season prior to winter on annual survival of an Adélie penguin population using mark-recapture models based on penguin tag and resight data over a sixteen year period. This analysis required an extension to the basic Cormack-Jolly-Seber model by incorporating age structure in recapture and survival sub-models. By including model covariates we show that survival of older penguins is primarily related to the amount and concentration of ice present in their winter foraging grounds. In contrast, fledgling and yearling survival depended on other factors in addition to the physical marine environment and outcomes of the previous breeding season but we were unable to determine what these were. The relationship between sea-ice and survival differed with penguin age: extensive ice during the return journey to breeding colonies was detrimental to survival for the younger penguins whereas either too little or too much ice (between 15 and 80% cover) in the winter foraging grounds was detrimental for adults. Our results demonstrate that predictions of Adélie penguin survival can be improved by taking into account penguin age, prior breeding conditions and environmental features.

The use of the catch per unit effort (CPUE) as an index of abundance usually requires a standardization process consisting of isolating all those exogenous factors from temporal variations in abundance from the CPUE time-series. These exogenous factors include those generated by modifications in fishery vessel efficiency, variations in fishing strategies, and environmental fluctuations. The selection of the latter has been considered to be one of the most difficult, arbitrary, and poorly documented stages since the environmental effects vary on different temporal scales in autocorrelated and non-random manners, influencing the CPUE through a cause-effect process. Transfer function models (TFM) were constructed to describe statistically the cause-effect relationship between two time-series and herein we propose that TFM are a valid tool for: i) discriminating environmental effects that influence the CPUE and ii) describing how these effects should be included in a generalized lineal model (GLM). We analyzed the Antarctic krill CPUE from August 1989 to July 1999, and as possible causal effects, the Antarctic Oscillation Index (AOI) and atmospheric pressure at sea level (APSL). TFM shows that the APSL, with an annual lag (APSL12), influences the CPUE of Antarctic krill, whereas the AOI did not have a significant effect. The use of APSL12 in the GLM increased the explanation of the deviance by 31% as compared with the APSL with no lag. We concluded that TFM constitute a promising tool for including environmental effects in the standardization of the CPUE that would result in less biased and more accurate indexes of abundance.

The occurrence of dwarf minke whales (Balaenoptera acutorostrata subsp.) around the Antarctic Peninsula was examined based on 406 sightings of minke whales recorded during the Chilean Antarctic Scientific Expeditions and other opportunistic cetacean surveys. Identification of the species was made only for the whales sighted in the proximity of the vessels when the specific diagnostic characters could be confirmed. Of the 406 sightings, 296 were assigned to Antarctic (519 individuals), nine (11 individuals) to dwarf and 101 to unidentified minke whales (149 individuals). Dwarf minke whales were identified by the reported external diagnostic characters for this species. Seven animals occurred around the South Shetland Island and four in the Gerlache Strait. In addition, another two animals were identified as dwarf minke whales in the Bellinghausen Sea in winter 1993, being these the most southern records for this species. These results confirm the occurrence of dwarf minke whales around the Antarctic Peninsula during the summer seasons, as well as in the Bellinghausen Sea in winter. The geographical range of these sightings was comprised between 61°03´ and 69°25´S and between 55°29´ and 86°53´W. These results also suggest that some dwarf minke whales remain in the Antarctic during the austral winter.

The West Antarctic Peninsula (WAP) and adjacent Scotia Sea support abundant wildlife populations, many of which were nearly extirpated by humans. This region is also among the fastest-warming areas on the planet, with 5–6 °C increases in mean winter air temperatures and associated decreases in winter sea-ice cover. These biological and physical perturbations have affected the ecosystem profoundly. One hypothesis guiding ecological interpretations of changes in top predator populations in this region, the “sea-ice hypothesis,” proposes that reductions in winter sea ice have led directly to declines in “ice-loving” species by decreasing their winter habitat, while populations of “ice-avoiding” species have increased. However, 30 y of field studies and recent surveys of penguins throughout the WAP and Scotia Sea demonstrate this mechanism is not controlling penguin populations; populations of both ice-loving Adélie and ice-avoiding chinstrap penguins have declined significantly. We argue in favor of an alternative, more robust hypothesis that attributes both increases and decreases in penguin populations to changes in the abundance of their main prey, Antarctic krill. Unlike many other predators in this region, Adélie and chinstrap penguins were never directly harvested by man; thus, their population trajectories track the impacts of biological and environmental changes in this ecosystem. Linking trends in penguin abundance with trends in krill biomass explains why populations of Adélie and chinstrap penguins increased after competitors (fur seals, baleen whales, and some fishes) were nearly extirpated in the 19th to mid-20th centuries and currently are decreasing in response to climate change.

The organization, example outputs, and future objectives of an integrated, age‐structured model designed to estimate krill population dynamics and productivity are described. The model's capabilities are illustrated using 19 years of survey data collected by the U.S. AMLR Program around the South Shetland Islands, but it is being developed to be applicable to any region where multi‐year data on size compositions from net tows and total biomass from hydroacoustics or net tows are available. The model estimates population parameters from data based on a joint likelihood function and is being developed in stages to incorporate different data sources. Model estimates are incremented for time of year in which the data were collected, with multiple surveys at different times within a year possible. Ages are converted to lengths based on a single age‐to‐length transition matrix. Currently the model assimilates data from research surveys but data from fisheries and eventually from krill predators and environmental time series will also be incorporated. Annual movement by krill among areas is included, but, currently, the model has convergence problems (is able to make point estimates but not variance estimates) when movement is included; converge can be achieved when movement is ignored.

One of the recommendations for future work from the 2008 Predator Survey Workshop was ‘....that alternative census methods for large (penguin) colonies may be helpful. Such methods include the use of satellite imagery and the use of GPS receivers to accurately map colony areas from which abundance could be estimated given known information on nest density within colonies...’ This paper uses virtual simulation in a GIS environment to explore how the recommended area/density approach can be optimally designed when abundance estimates are required for large colonies or over large scales. The approach is illustrated using a case study of a large-scale ground survey of Adélie penguins. The findings implications for optimising designs over much larger areas using alternate platforms such as satellites and aircraft.

The influence of the climate change on krill based Southern Ocean ecosystem is studied intensively last decade. To study the possible connections between Antarctic Peninsula regional climate warming and ecosystem changes the data of Antarctic krill density (KRILLBASE data) and of surface air temperature (READER data) were used. Decadal variability of winter temperature on the regional scale was analyzed. We apply Fourier and wavelet analysis to the averaged temperature anomaly time series and note the oscillations with 3-8 year periods and a decadal oscillation with a period of about 16 years. Preliminary analysis of Antarctic krill density variations shows about 8-year variability as well. Correlation coefficient between krill density data and temperature anomalies is small (less 0.2) however is increased significantly to 0.4 when applying 1-year data shift (krill density after anomalies occurrence). The 3-8 year periods possibly related to the ENSO variations and connected to sea ice change.

Species composition and abundance of by-catch fish were analyzed using the fish by-catch samples collected by scientific observers onboard Japanese commercial krill fishing vessels in the north of South Georgia during the austral winter from 2002 to 2008. A total of 19 species belonging to 8 families were identified in the by-catch samples, among which the 3 species, Krefftichthys anderssoni, Lepidonotothen larseni and Champsocephalus gunnari, were recorded in every year from 2002 to 2008, and also the most abundant. Different life stages of K. anderssoni, Gymnoscopelus nicholsi, C. gunnari and L. larseni from larvae to adults were found, suggesting that these species may have their nursery ground around South Georgia. The length frequency distribution of C. gunnari varied with years and may indicate the increase in size in recent years. Electrona antarctica was not a major component of the recent mesopelagic ichthyofauna. In contrast, P. choriodon, which is known as a South temperate species, became to dominate the recent samples. Additionally, the size distribution of P. choriodon was unimodal, indicating that the individuals probably migrated from the population in northern warmer areas. Since the distribution patterns and biological peculiarity of fish are related to oceanographic conditions, the shifts in species and size composition may demonstrate oceanographic and climatic changes in the Antarctic Ocean. Long-term monitoring of ichthyofauna through the scientific observer program and close examination of the biological samples would provide important information on environmental fluctuations in the Antarctic Ocean.