Multifactor synergistic drivers of first arrival dates in Chinese birds: Nationwide evidence from audio-video fusion monitoring

We leveraged deep-learning-enabled autonomous acoustic recorders and high-definition cameras to quantify how climate, upper-level winds, and vegetation cover influence first arrival dates (FADs) of birds across the subtropical-temperate transition zone of China. From December 2021 to June 2025, we conducted continuous monitoring at 81 wetland and forest stations spanning 22-45° N and established an end-to-end workflow covering data acquisition, quality control, modeling, and validation. In total, we extracted 5959 FADs encompassing 618 resident and migratory species along the East Asian-Australasian Flyway. We linked FADs to daily mean temperature, daily cumulative precipitation, tailwind anomaly, crosswind strength, and vegetation anomaly. Using covariates averaged over a backward 30-day window, we fitted linear mixed-effects models (LMMs) and generalized additive mixed models (GAMMs) with species and region as random effects. Results were consistent across model classes: warming is associated with delayed arrivals-each +1 °C in 30-day mean temperature corresponds to a ∼2.58-3.60-day delay in FAD. Precipitation and stronger crosswinds likewise tend to delay arrival, whereas higher vegetation anomalies and favorable tailwinds advance arrival. Collectively, our findings clarify how climate, upper-level wind fields, and vegetation jointly shape avian phenology across continental scales.