Clustering community science data to infer songbird migratory connectivity in the Western Hemisphere

Abstract

Abstract Migratory connectivity describes the spatial linkage among migrating individuals through time. Accounting for it is necessary for full annual cycle conservation planning, to avoid uneven protection leading to overall population declines. However, conventional methods used to study migratory connectivity usually demand substantial fiscal and human resources. We present a methodology that infers patterns of migratory connectivity for songbirds using relative abundance models created from eBird, a global community science program. We compare our inferences with previously described patterns of migratory connectivity for two species assumed to exhibit broadscale parallel migration strategies: wood thrush (Hylocichla mustelina) and Wilson's warbler (Cardellina pusilla). Initial findings suggest that our method has the potential to be a rapid and inexpensive way to infer broad patterns of connectivity for species that do not engage in leapfrog migration nor deviate much from parallel migration. Our flexible framework can be used to guide sampling designs for studies of migratory connectivity and to generate hypotheses for species in need of urgent conservation planning for which migratory connectivity has not yet been established.

Keywords

• Bayesian analysis
• birds
• community science
• connectivity
• conservation
• migration