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ABSTRACT Partial migration is a phenomenon where migratory and resident individuals of the same species co‐exist within a population, and has been linked to both intrinsic (e.g., genetic) as well as environmental factors. Here we investigated the genomic architecture of partial migration in the common blackbird, a songbird that comprises resident populations in the southern distribution range, partial migratory populations in central Europe, and exclusively migratory populations in northern and eastern Europe. We generated whole‐genome sequencing data for 60 individuals, each of which was phenotyped for migratory behavior using radio‐telemetry tracking. These individuals were sampled across the species' distribution range, including resident populations (Spain and France), obligate migrants (Russia), and a partial migratory population with equal numbers of migratory and resident individuals in Germany. We estimated genetic differentiation (F_ST) of single‐nucleotide variants (SNVs) in 2.5 kb windows between all possible population and migratory phenotype combinations, and focused our characterization on birds from the partial migratory population in Germany. Despite overall low differentiation within the partial migratory German population, we identified several outlier regions with elevated differentiation on four distinct chromosomes. The region with the highest relative and absolute differentiation was located on chromosome 9, overlappingPER2, which has previously been shown to be involved in the control of the circadian rhythm across vertebrates. While this region showed high levels of differentiation, no fixed variant could be identified, supporting the notion that a complex phenotype such as migratory behavior is likely controlled by a large number of genetic loci. 

Over the past five decades, a large number of wild animals have been individually identified by various observation systems and/or temporary tracking methods, providing unparalleled insights into their lives over both time and space. However, so far there is no comprehensive record of uniquely individually identified animals nor where their data and metadata are stored, for example photos, physiological and genetic samples, disease screens, information on social relationships.Databases currently do not offer unique identifiers for living, individual wild animals, similar to the permanent ID labelling for deceased museum specimens.To address this problem, we introduce two new concepts: (1) a globally unique animal ID (UAID) available to define uniquely and individually identified animals archived in any database, including metadata archived at the time of publication; and (2) the digital ‘home’ for UAIDs, the Movebank Life History Museum (MoMu), storing and linking metadata, media, communications and other files associated with animals individually identified in the wild. MoMu will ensure that metadata are available for future generations, allowing permanent linkages to information in other databases.MoMu allows researchers to collect and store photos, behavioural records, genome data and/or resightings of UAIDed animals, encompassing information not easily included in structured datasets supported by existing databases. Metadata is uploaded through the Animal Tracker app, the MoMu website, by email from registered users or through an Application Programming Interface (API) from any database. Initially, records can be stored in a temporary folder similar to a field drawer, as naturalists routinely do. Later, researchers and specialists can curate these materials for individual animals, manage the secure sharing of sensitive information and, where appropriate, publish individual life histories with DOIs. The storage of such synthesized lifetime stories of wild animals under a UAID (unique identifier or ‘animal passport’) will support basic science, conservation efforts and public participation. 

Abstract Quantifying movement and demographic events of free‐ranging animals is fundamental to studying their ecology, evolution and conservation. Technological advances have led to an explosion in sensor‐based methods for remotely observing these phenomena. This transition to big data creates new challenges for data management, analysis and collaboration.We present the Movebank ecosystem of tools used by thousands of researchers to collect, manage, share, visualize, analyse and archive their animal tracking and other animal‐borne sensor data. Users add sensor data through file uploads or live data streams and further organize and complete quality control within the Movebank system. All data are harmonized to a data model and vocabulary. The public can discover, view and download data for which they have been given access to through the website, the Animal Tracker mobile app or by API. Advanced analysis tools are available through the EnvDATA System, the MoveApps platform and a variety of user‐developed applications. Data owners can share studies with select users or the public, with options for embargos, licenses and formal archiving in a data repository.Movebank is used by over 3,100 data owners globally, who manage over 6 billion animal location and sensor measurements across more than 6,500 studies, with thousands of active tags sending over 3 million new data records daily. These data underlie >700 published papers and reports. We present a case study demonstrating the use of Movebank to assess life‐history events and demography, and engage with citizen scientists to identify mortalities and causes of death for a migratory bird.A growing number of researchers, government agencies and conservation organizations use Movebank to manage research and conservation projects and to meet legislative requirements. The combination of historic and new data with collaboration tools enables broad comparative analyses and data acquisition and mapping efforts. Movebank offers an integrated system for real‐time monitoring of animals at a global scale and represents a digital museum of animal movement and behaviour. Resources and coordination across countries and organizations are needed to ensure that these data, including those that cannot be made public, remain accessible to future generations.