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<p>Accurate prediction of plants’ flowering onset date (FOD) is vital for maintaining ecosystem functions and boosting forestry economic gains. While the Spring Warming (SW) model is commonly used to predict flowering phenology, its traditional fixed setting of the heat accumulation threshold (<i>HAT</i>), measured by growing degree days (<i>GDD</i>), fails to account for the spatial variation in preseason thermal requirements reported in previous studies. This limitation reduces the accuracy of FOD predictions across large spatial areas. In this study, we hypothesized that the <i>HAT</i> in the SW model varies spatially with habitat-specific temperature due to thermal acclimation. To test this, we systematically quantified the spatial differences in <i>HAT</i> using observed FOD data of <i>Robinia pseudoacacia</i>, which is a keystone species for afforestation and a vital nectar source, from 58 stations across China between 1963 and 2008. We identified the key temperature variables influencing <i>HAT</i> variability and developed a simplified, spatially dynamic <i>HAT</i> scheme. The updated SW model, incorporating this variable <i>HAT</i>, was evaluated with cross-site FOD observations. Results showed significant variation of <i>HAT</i> across different climate zones. A geodetector analysis found that the mean temperature from February to May was the main factor driving <i>HAT</i> heterogeneity, supporting our hypothesis. Additionally, spatial factors such as elevation and longitude also contributed to <i>HAT</i> variation alongside thermal factors. Incorporating this spatially variable <i>HAT</i>, predicted from preseason temperatures, into the SW model significantly improved FOD prediction accuracy, decreasing the root mean square error (<i>RMSE</i>) by 11.91% compared to a model with a constant <i>HAT</i>. Future climate scenario predictions indicated that the SW mode with the fixed <i>HAT</i> underestimated FOD advances in warmer areas and overestimated the rate of change, especially when compared to the heterogeneous <i>HAT</i> model. Overall, we emphasize the importance of considering spatial thermal acclimation in broad-scale flowering onset predictions.</p>

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Enhancing broad-scale prediction of flowering onset by incorporating spatial heterogeneity in heat accumulation threshold

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Jiaxin Jin,
  • Yuting Hao,
  • Qiuan Zhu,
  • Weifeng Wang,
  • Yuanwei Qin,
  • Long Hai,
  • Zhuofan Li,
  • Jin Wu

摘要

Accurate prediction of plants’ flowering onset date (FOD) is vital for maintaining ecosystem functions and boosting forestry economic gains. While the Spring Warming (SW) model is commonly used to predict flowering phenology, its traditional fixed setting of the heat accumulation threshold (HAT), measured by growing degree days (GDD), fails to account for the spatial variation in preseason thermal requirements reported in previous studies. This limitation reduces the accuracy of FOD predictions across large spatial areas. In this study, we hypothesized that the HAT in the SW model varies spatially with habitat-specific temperature due to thermal acclimation. To test this, we systematically quantified the spatial differences in HAT using observed FOD data of Robinia pseudoacacia, which is a keystone species for afforestation and a vital nectar source, from 58 stations across China between 1963 and 2008. We identified the key temperature variables influencing HAT variability and developed a simplified, spatially dynamic HAT scheme. The updated SW model, incorporating this variable HAT, was evaluated with cross-site FOD observations. Results showed significant variation of HAT across different climate zones. A geodetector analysis found that the mean temperature from February to May was the main factor driving HAT heterogeneity, supporting our hypothesis. Additionally, spatial factors such as elevation and longitude also contributed to HAT variation alongside thermal factors. Incorporating this spatially variable HAT, predicted from preseason temperatures, into the SW model significantly improved FOD prediction accuracy, decreasing the root mean square error (RMSE) by 11.91% compared to a model with a constant HAT. Future climate scenario predictions indicated that the SW mode with the fixed HAT underestimated FOD advances in warmer areas and overestimated the rate of change, especially when compared to the heterogeneous HAT model. Overall, we emphasize the importance of considering spatial thermal acclimation in broad-scale flowering onset predictions.