| Abstract: | Despite declines in numerous migratory bird populations due to global climate and landscape changes, the Pacific Flyway population of Greater White‐fronted Geese Anser albifrons frontalis in North America has flourished over recent decades. However, the demographic foundations of the population increase remain unclear, largely due to sparse data. In this study, we used a Bayesian integrated population model (IPM) to maximize information from multiple data sources including coordinated population survey, ring‐recovery and hunter‐harvested goose tail data. We estimated demographic parameters and assessed the role of several possible drivers of the observed population increase, including density‐dependent processes, agricultural land use change and climate conditions in both the wintering and the breeding season, while also accounting for the impacts of harvest. Non‐harvest survival of all geese was 0.83 (95% credible interval (CRI): 0.70–0.96) before legislation restricted post‐harvest rice field burning, and 0.98 (95% CRI: 0.94–1.0) afterwards. We detected a negative effect of density‐dependent processes and a positive effect of El Niño‐Southern Oscillation on non‐harvest survival with high certainty. Kill rates were 0.11 (95% CRI: 0.09–0.12) for adults (after hatch year) and 0.26 (95% CRI 0.21–0.31) for juveniles (hatch year), resulting in annual survival rates of 0.81 (95% CRI: 0.69–0.89) for adults and 0.67 (95% CRI: 0.56–0.76) for juveniles. The ratio of juvenile birds to adults in the population was on average 0.36 (95% CRI: 0.29–0.45) and was driven by negative density‐dependent processes with high certainty. Our results suggest that the ban on rice field burning and subsequent high frequency of flooding as an alternative rice decomposition practice was the primary driver of the Pacific white‐fronted Goose population increase. The effects of climate change and density dependence were not strong enough to suppress the benefit of flooded rice. Given sparse demographic data for Pacific white‐fronted Geese, we were only able to uncover drivers of demography using IPMs. We encourage practitioners with sparse data similarly to consider forming IPMs to determine the drivers and mechanisms for population change and to prioritize future data collection. |
