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 (βT and βP) might be biased and their variability across sites and species are not fully explained. We benchmarked species-level βT and βP statistically inferred from historical records with observations from a field manipulative experiment. We then analyzed landscape scale βT and βP 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 βT by 2.36-3.87 d °C-1 (54-88%) while process-based phenology model fitting predicts comparable βT and βP. Process-based estimates of βT and βP are negatively correlated across species (R = -0.94, P < 0.01) and across sites (R = -0.45, P < 0.01). βT is positively correlated with mean annual temperature, and βP 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 βT and βP contributes to species-level and site-level variabilities in phenological sensitivities in alpine grasslands, which needs to be incorporated in predicting future phenological changes.