Barbara Helm

Abstract Advancements in mass spectrometry (MS) technologies have significantly improved the ability to quantify proteins and analyse their modifications. However, MS-based proteomics datasets frequently encounter missing values due to a complex interplay of missing at random (MAR) and missing not at random (MNAR) mechanisms. Such missing data can result in information loss and biased outcomes in data pre-processing, as well as subsequent analyses and interpretations.

Introduction Annual cycles of organisms have evolved to align seasonal activities with predictably fluctuating environmental conditions. As environments are rapidly changing, there is strong evidence that plants and animals are adjusting their phenology, i.e. the timing of seasonal processes (Walther et al. 2002). Although such adjustments occur at global scales, they typically differ between phases of the annual cycle, geographic locations and taxonomic groups (Walther et al. 2002, Thackeray et al.

For over a century the study of bird migration has attracted both scientists and the wider public. This fascination comes from the extraordinary distances covered by migratory species, the precision of avian navigation and the near clockwork timing punctuality on arrival times, all of which continue to inspire awe. At the same time the physiological adaptations that enable these journeys remain an active frontier of research.