Dominance in feeding territories relates to foraging success and offspring growth in brown skuas <Emphasis Type="Italic">Catharacta antarctica lonnbergi</Emphasis>

Territoriality should lead to strict dominance, as territory holders typically control access to resources and exclude others from their use. In feeding territories, dominance should be reflected in foraging success and ultimately in reproduction differences; however, these successive links have rarely been made explicit. Therefore, we investigated a population of brown skuas Catharacta antarctica lonnbergi, in which only part of the breeding population occupied feeding territories within penguin colonies. We identified the dominance hierarchy and determined the foraging success of the participants in fights for access to penguin carcasses within the territories. Furthermore, we monitored offspring growth from parents with and without feeding territories. Our results indicated a clear dominance hierarchy with territorial birds in their own territory dominating over territorial breeders from other territories, non-territorial breeders and non-breeding birds. However, territory owners could not completely exclude others from access to food. Foraging success was positively related to dominance scores: The dominant territory owners received 63% of a carcass, whereas non-territorial pairs could get less than 10%. The link between foraging success and offspring development was less clear: Although male chicks of non-territorial parents suffered from lower growth rates and, thus, delayed fledging, there were no such differences in female chicks. Territoriality in skuas did not imply a complete occupation of food, but guaranteed optimal growth conditions for offspring. Non-territorial individuals were forced to search for alternative resources, and the restricted access to the preferred food resulted in inferior conditions for offspring development, making this foraging strategy less rewarding.     

Fault detection approach for systems involving soft sensors

This paper develops a new approach for fault detection which involves soft sensors for process monitoring. Unlike existing approaches, which compare current measurements, or linear combinations thereof, to values of these measurements representing normal operations, the methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the effect of abnormal process conditions on the state variables can be directly observed and that it is possible to include nonlinear relationships between measurements and states. At the same time, this type of approach has the drawback that the variances of the unmeasured states are not equal to the variances of the actual process variables due to the use of a soft sensor. However, for many popular soft sensor techniques, such as Kalman filters and related approaches, it is possible to compute variances of the predicted states that correspond to normal operating conditions. This paper presents a general framework for using soft sensors for process monitoring, i.e., soft sensor design and computation of the statistics that represent normal operating conditions, and illustrates this framework in three specific applications. It should be pointed out that the contribution of this work does not lie with the soft sensor design or the computation of the statistics itself as either part has individually already been addressed in the existing literature. However, the authors are not aware of any studies where both tasks are combined for process monitoring, which forms the contribution of this work.