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

Abstract

• 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.