Quantifying long-term phenological patterns of aerial insectivores roosting in the Great Lakes region using weather surveillance radar.

ABSTRACT: Organisms have been shifting their timing of life history events (phenology) in response to changes in the emergence of resources induced by climate change. Yet understanding these patterns at large scales and across long time series is often challenging. Here we used the US weather surveillance radar network to collect data on the timing of communal swallow and martin roosts and evaluate the scale of phenological shifts and its potential association with temperature. The discrete morning departures of these aggregated aerial insectivores from ground-based roosting locations are detected by radars around sunrise. For the first time, we applied a machine learning algorithm to automatically detect and track these large-scale behaviors. We used 21 years of data from 12 weather surveillance ra

SUBMITTER: Deng Y 

PROVIDER: S-EPMC10098490 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature