Path characteristics and colony arrival-departure statistics of king penguins (Aptenodytes patagonicus) on Possession and Kerguelen Island

Orientation based on visual cues can be extremely difficult in crowded bird colonies due to the presence of many individuals. We studied king penguins (Aptenodytes patagonicus) that live in dense colonies and are constantly faced with such problems. Our aims were to describe adult penguin homing paths on land and to test whether visual cues are important for their orientation in the colony. We also tested the hypothesis that older penguins should be better able to cope with limited visual cues due to their greater experience. We collected and examined GPS paths of homing penguins. In addition, we analyzed 8 months of penguin arrivals to and departures from the colony using data from an automatic identification system. We found that birds rearing chicks did not minimize their traveling time on land and did not proceed to their young (located in creches) along straight paths. Moreover, breeding birds' arrivals and departures were affected by the time of day and luminosity levels. Our data suggest that king penguins prefer to move in and out of the colony when visual cues are available. Still, they are capable of navigating even in complete darkness, and this ability seems to develop over the years, with older breeding birds more likely to move through the colony at nighttime luminosity levels. This study is the first step in unveiling the mysteries of king penguin orientation on land.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.