Long‐term analysis of persistence and size of swallow and martin roosts in the US Great Lakes

In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications. After they finish breeding in North America, before starting their southbound migration, swallows and martins gather in large communal roosts that congregate thousands to hundreds of thousands of individuals every day. These aggregations are easily seen on Weather Surveillance Radars, where they produce a characteristic donut‐shaped signature. In this study, we employ machine learning and radar theory to locate and quantify the number of birds joining each roost in the Great Lakes region from 2000 to 2020. We characterize this communal behavior in terms of its size and persistence in the landscape, describing how regions that were used year‐after‐year also had higher overall activity each year.