Trade-off between spring phenological sensitivities to temperature and precipitation across species and space in alpine grasslands over the Qinghai–Tibetan Plateau

Elucidating climatic drivers of spring phenology in alpine grasslands is critical. However, current statistical estimates of spring phenological sensitivities to temperature and precipitation (β_T and β_P) might be biased and their variability across sites and species are not fully explained. We benchmarked species-level β_T and β_P statistically inferred from historical records with observations from a field manipulative experiment. We then analyzed landscape scale β_T and β_P estimated from the best statistical approach in the benchmark analysis across 57 alpine grassland sites in the Qinghai–Tibetan Plateau. Compared with manipulative experiment results, process-agnostic regression-based approaches underestimate β_T by 2.36–3.87 d °C⁻¹ (54–88%) while process-based phenology model fitting predicts comparable β_T and β_P. Process-based estimates of β_T and β_P are negatively correlated across species (R = −0.94, P < 0.01) and across sites (R = −0.45, P < 0.01). β_T is positively correlated with mean annual temperature, and β_P is negatively correlated with elevation at the regional scale. Using process-based model fitting can better estimate spring phenological sensitivities to climate. The trade-off between β_T and β_P contributes to species-level and site-level variabilities in phenological sensitivities in alpine grasslands, which needs to be incorporated in predicting future phenological changes.