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Global migrations of diverse animal species often converge along the same routes, bringing together seasonal assemblages of animals that may compete, prey on each other, and share information or pathogens. These interspecific interactions, when energetic demands are high and the time to complete journeys is short, may influence survival, migratory success, stopover ecology, and migratory routes. Numerous accounts suggest that interspecific co-migrations are globally distributed in aerial, aquatic, and terrestrial systems, although the study of migration to date has rarely investigated species interactions among migrating animals. Here, we test the hypothesis that migrating animals are communities engaged in networks of ecological interactions. We leverage over half a million records of 50 bird species from five bird banding sites collected over 8 to 23 y to test for species associations using social network analyses. We find strong support for persistent species relationships across sites and between spring and fall migration. These relationships may be ecologically meaningful: They are often stronger among phylogenetically related species with similar foraging behaviors and nonbreeding ranges even after accounting for the nonsocial contributions to associations, including overlap in migration timing and habitat use. While interspecific interactions could result in costly competition or beneficial information exchange, we find that relationships are largely positive, suggesting limited competitive exclusion at the scale of a banding station during migratory stopovers. Our findings support an understanding of animal migrations that consist of networked communities rather than random assemblages of independently migrating species, encouraging future studies of the nature and consequences of co-migrant interactions. 

Abstract Insects are the most ubiquitous and diverse group of eukaryotic organisms on Earth, forming a crucial link in terrestrial and freshwater food webs. They have recently become the subject of headlines because of observations of dramatic declines in some places. Although there are hundreds of long‐term insect monitoring programs, a global database for long‐term data on insect assemblages has so far remained unavailable. In order to facilitate synthetic analyses of insect abundance changes, we compiled a database of long‐term (≥10 yr) studies of assemblages of insects (many also including arachnids) in the terrestrial and freshwater realms. We searched the scientific literature and public repositories for data on insect and arachnid monitoring using standardized protocols over a time span of 10 yr or longer, with at least two sampling events. We focused on studies that presented or allowed calculation of total community abundance or biomass. We extracted data from tables, figures, and appendices, and, for data sets that provided raw data, we standardized trapping effort over space and time when necessary. For each site, we extracted provenance details (such as country, state, and continent) as well as information on protection status, land use, and climatic details from publicly available GIS sources. In all, the database contains 1,668 plot‐level time series sourced from 165 studies with samples collected between 1925 and 2018. Sixteen data sets provided here were previously unpublished. Studies were separated into those collected in the terrestrial realm (103 studies with a total of 1,053 plots) and those collected in the freshwater realm (62 studies with 615 plots). Most studies were from Europe (48%) and North America (29%), with 34% of the plots located in protected areas. The median monitoring time span was 19 yr, with 12 sampling years. The number of individuals was reported in 129 studies, the total biomass was reported in 13 studies, and both abundance and biomass were reported in 23 studies. This data set is published under a CC‐BY license, requiring attribution of the data source. Please cite this paper if the data are used in publications, and respect the licenses of the original sources when using (part of) their data as detailed in Metadata S1: Table 1.