Interpreting the influences of multiple factors on forcing requirements of leaf unfolding date by explainable machine learning algorithms
文献类型：期刊论文
| 作者 | Gao, Chengxi1,2; Wang, Huanjiong2; Ge, Quansheng2; Dai, Junhu2 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 166页码:112402 |
| 关键词 | Leaf unfolding date Spring phenology Machine learning Phenological model Chilling Climate change |
| DOI | 10.1016/j.ecolind.2024.112402 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Understanding how spring phenology of temperate trees responds to global change is essential for assessing the vegetation dynamics in the future. Besides chilling and forcing temperatures, the influences of other environmental and biotic factors on leaf unfolding date (LUD) are still unclear. Based on long-term records (1960-2015) of LUD for 6 typical temperate tree species at 4242 stations in Europe, we first used a classic process-based model (the Unified model) to describe the relationship between chilling and forcing requirements of LUD. Furthermore, we used 3 explainable machine learning (ML) algorithms (RF, EBM, and GAMI-Net) to quantify the influences of 45 biotic and environmental factors on the LUD. The root-mean-square error (RMSE) of the ML-based models averaged from all species (7.03 to 7.33 days) was lower than the Unified model (8.73 days). The ML algorithms detected 2 biotic (elevation and previous leaf senescence date) and 3 temperature-related variables (chilling accumulation, freezing days in March and annual temperature range) with high importance for most species. The trees adapted to a higher elevation or with later leaf senescence date in the previous year exhibited later LUD. The increase in chilling accumulation, freezing days in March and the annual temperature range could reduce the forcing requirement of LUD. Water-related and light-related variables were only important for one or two species. The increase in relative humidity in February could advance LUD, while soil moisture in March had the opposite effect. The increase in March day length and downward surface shortwave radiation exerted an advancing influence on the LUD of light-sensitive species. Our results suggest that incorporating multiple biotic and environmental factors into ML algorithms not only could effectively improve the accuracy of phenological prediction but also enhance our understanding of how spring phenology responds to each factor. |
| WOS关键词 | SPRING PHENOLOGY ; CLIMATE-CHANGE ; TREES ; POPULATIONS ; PHOTOPERIOD ; FRAMEWORK ; HUMIDITY ; GROWTH ; MODEL ; ONSET |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001281862800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206898]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Wang, Huanjiong |
| 作者单位 | 1.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Chengxi,Wang, Huanjiong,Ge, Quansheng,et al. Interpreting the influences of multiple factors on forcing requirements of leaf unfolding date by explainable machine learning algorithms[J]. ECOLOGICAL INDICATORS,2024,166:112402. |
| APA | Gao, Chengxi,Wang, Huanjiong,Ge, Quansheng,&Dai, Junhu.(2024).Interpreting the influences of multiple factors on forcing requirements of leaf unfolding date by explainable machine learning algorithms.ECOLOGICAL INDICATORS,166,112402. |
| MLA | Gao, Chengxi,et al."Interpreting the influences of multiple factors on forcing requirements of leaf unfolding date by explainable machine learning algorithms".ECOLOGICAL INDICATORS 166(2024):112402. |
入库方式： OAI收割
来源：地理科学与资源研究所
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