Integrating regional and site-level data to assess drivers of population decline in a threatened aerial insectivorous bird. (January 2022) - Zhao, Qing; Mitchell, Greg W.; Cadman, Michael D.; Kusack, Jackson W.; Evans, Dean R.; Bumelis, Kaelyn; Hobson, Keith A.; Weegman, Mitch D.; Salvadori, Antonio; Wilson, Scott
Abstract: Knowledge of demographic processes is essential for understanding population dynamics and developing appropriate conservation strategies under global change. However, it is often challenging to collect the necessary population and demographic data to understand population dynamics. Integrated population modeling (IPM) provides an opportunity to combine population survey and demographic data from different sources. We developed an IPM and applied it to jointly analyze 15 years (2004–2018) of regional North American Breeding Bird Survey and site-level capture-recapture and age-ratio data for a barn swallow ( Hirundo rustica ) population in Ontario, Canada. We aimed to identify key demographic parameters and tested hypothesized environmental drivers to understand population declines of this species. Our results showed that the study population had declined by about 2% annually during our study period, which was mainly contributed by changes in net immigration and adult apparent survival. In particular, low net immigration in a few years of our study period seemed to be the main reason of population decline. Furthermore, we found that warmer temperatures in April when barn swallows return to breeding grounds had a positive effect on adult survival, while warmer temperatures during the breeding period in May–June had a negative effect on productivity. Our results indicate vulnerability of the species to inclement weather during the breeding season, suggesting potential population recovery by adopting conservation strategies that buffer climatic threats. Moreover, our IPM provides a foundation for future monitoring programs that intend to combine and maximize the utility of large-scale population counts and fine-scale demographic data.