Challenges and benefits of using unstructured citizen science data to estimate seasonal timing of bird migration across large scales

Abstract

Millions of bird observations have been entered on online portals in the past 20 years either as checklists or arbitrary individual entries. While several hundred publications have been written on a variety of topics based on bird checklists worldwide, unstructured non-checklist observations have received little attention and praise by academia. In the present study we tested the suitability of non-checklist data to estimate key figures of large-scale migration phenology in four zones covering the whole of Finland. For that purpose, we analysed 10 years of ornithological non-checklist data including over 400 million. individuals of 115 bird species. We discuss bird- and human-induced effects to be considered in handling non-checklist data in this context and describe applied methodologies to address these effects. We calculated 5%, 50% and 95% percentile dates of spring and autumn migration period for all species in all four zones. For validation purposes we compared the temporal distributions of 43 bird species with migration phenology from standardized long-term ringing data in autumn of which 24 species (56%) showed similar medians. In a model approach, non-checklist data successfully revealed latitudinal migration progression in spring and autumn. Overall, non-checklist data proved to be well suited to determine descriptors of migration phenology in Northern Europe which are challenging to attain by any other currently available means. The effort-to-yield ratio of data processing was commensurate to the outcomes. The unprecedented spatiotemporal coverage makes non-checklist data a valuable complement to current migration databases from bird observatories. The basic concept of the present methodology is applicable to data from other bird portals, if combined with local field ornithological knowledge and literature. Species-specific descriptors of migration phenology can be potentially used in climate change studies and to support echo interpretation in radar ornithology.