Canopy structure regulates autumn phenology by mediating microclimate in temperate forests

Data Descriptions Here, we provide the data and code used for the paper “Canopy structure regulates autumn phenology by mediating microclimate in temperate forests” by Wu et al. Code and Data folders includes all the code and data for analysis in the main text, organized by: Fig1_DataAnalysis.R and Fig1.ipynb contain code for analyzing the spatial variations of autumn phenology metrics and relationships between canopy structure metrics and autumn phenology. Related datasets include Data1_Cano_Micro_Phe.csv, Data1_DataSlope.csv, Data1_DataYear.csv, Data1_Mean_Phenology.csv, and Data1_variance_explained.csv. Fig2_DataAnalysis.R and Fig2.ipynb contain code for analyzing the relationships between canopy structure and microclimate factors and relative contributions of microclimate factors to autumn phenology metrics. Related datasets include Data2_all_PAI_GLI.csv, Data2_all_pai_Tbuffer.csv, Data2_DataOneYear.csv, and Data2_Var_importance.csv. Fig3_DataAnalysis.R and Fig3.ipynb contain code for analyzing the relationships between microclimate factors and autumn phenology metrics. Related datasets include Data3_DataSlope.csv and Data3_DataYear.csv. Fig5_DataAnalysis.R and Fig5.ipynb contain code for comparing the accuracy of the phenology model and projecting future autumn phenology under the shared socio-economic pathway (SSP) 1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Related datasets include Data5_all_Preds.csv, Data5_AllData_Micro_Macro_Cano.csv, Data5_AllFutureFallDOY.csv, Data5_DelayDOY.csv, Data5_FutureFallDOY_roll.csv, Data5_Model_Pvalue.csv, Data5_ModelAccuracy.csv, and Data5_Ttest_model_accuracy.csv. SupplementaryAnalysis.R and SupplementaryFigs.ipynb contain code for analyzing 1.Spatial variations of autumn phenology metrics in the Changbai Mountain (CBS) site. Related datasets include SupplementaryData.pickle and ED1_CBS_all_data_sp_scale.csv. 2.Relationships between canopy structure, microclimate and autumn phenology metrics within the CBS site. Related datasets include SupplementaryData.pickle, ED2_CBS_Canopy.csv, ED2_CBS_Microclimate.csv, and ED2_CBSData.csv 3.Spatial variations of autumn phenology metrics categorized by tree species within the five NEON study sites. Related datasets include SupplementaryData.pickle and Datas2_DataSpecies.csv. 4.Distributions of canopy structure and simulated microclimate metrics within the five NEON study sites. Related datasets include SupplementaryData.pickle and Datas3_DataYear.csv 5.Accuracy assessment of simulated microclimate factors. Related datasets include SupplementaryData.pickle, Datas4_allData_Meas_Simu.csv, Datas4_GLI_accuracy.csv and Datas4_Tbuffer_accuracy.csv. 6.Partial dependence plots demonstrating the response of autumn phenology metrics to microclimate factors. Related datasets include SupplementaryData.pickle, Datas5_PDP_DataYear.csv, Datas5_partial_dof.csv, Datas5_partial_sof.csv. 7.Pathways linking canopy structure and autumn phenology through mediating microclimate conditions within the five NEON study sites. Related datasets include Datas6_SEMData.csv. 8.Relationships between autumn phenology metrics and microclimate factors simulated at different heights within the five NEON study. Related datasets include SupplementaryData.pickle, Datas7_AllData_heights.csv and Datas7_DiffHeights.csv. 9.Correlation between start of autumn (SOA) and duration of autumn (DOA) in 2019 within the five NEON study sites. Related datasets include SupplementaryData.pickle and Datas8_SOFDOF_DataYear.csv. 10.Relationships between microclimate factors and end of autumn (EOA) within the five NEON study sites in 2019. Related datasets include SupplementaryData.pickle, Datas9_MiroEOF_DataYear.csv and Datas9_Micro_Lmm.csv. 11.Temporal changes of microclimate conditions with the progression of autumn phenological transition. Related datasets include SupplementaryData.pickle and Datas10_all_micro_time.csv. 12.Selection of areas with similar elevations for all statistical analyses in the current study. Related datasets include SupplementaryData.pickle and Datas12_DTMData.csv. Maps folder includes all autumn phenology data (SOA, DOA, and EOA) from 2018 to 2022 within the five National Ecological Observatory Network (NEON) study sites and the CBS site for 2020. Note: SOF (start of fall), DOF (duration of fall), and EOF (end of fall) used in the code share the same meaning of SOA, DOA, and EOA.数据集说明：本文提供了Wu等人发表的论文《温带森林冠层结构通过调控微气候影响秋季物候》（Canopy structure regulates autumn phenology by mediating microclimate in temperate forests）所使用的数据集与代码。 Code与Data文件夹包含正文分析所需的全部代码与数据，按研究内容组织如下： 1. Fig1_DataAnalysis.R与Fig1.ipynb：用于分析秋季物候指标的空间变异，以及冠层结构指标与秋季物候间的关联关系。相关数据集包括Data1_Cano_Micro_Phe.csv、Data1_DataSlope.csv、Data1_DataYear.csv、Data1_Mean_Phenology.csv与Data1_variance_explained.csv。 2. Fig2_DataAnalysis.R与Fig2.ipynb：用于分析冠层结构与微气候因子间的关联，以及微气候因子对秋季物候指标的相对贡献。相关数据集包括Data2_all_PAI_GLI.csv、Data2_all_pai_Tbuffer.csv、Data2_DataOneYear.csv与Data2_Var_importance.csv。 3. Fig3_DataAnalysis.R与Fig3.ipynb：用于分析微气候因子与秋季物候指标间的关联关系。相关数据集包括Data3_DataSlope.csv与Data3_DataYear.csv。 4. Fig5_DataAnalysis.R与Fig5.ipynb：用于对比物候模型的预测精度，并在共享社会经济路径（Shared Socio-economic Pathway, SSP）1-2.6、SSP3-7.0及SSP5-8.5情景下预估未来秋季物候。相关数据集包括Data5_all_Preds.csv、Data5_AllData_Micro_Macro_Cano.csv、Data5_AllFutureFallDOY.csv、Data5_DelayDOY.csv、Data5_FutureFallDOY_roll.csv、Data5_Model_Pvalue.csv、Data5_ModelAccuracy.csv与Data5_Ttest_model_accuracy.csv。 5. 补充分析代码SupplementaryAnalysis.R与补充图代码SupplementaryFigs.ipynb包含以下12项分析的相关代码： 1. 长白山（Changbai Mountain, CBS）样地秋季物候指标的空间变异，相关数据集包括SupplementaryData.pickle与ED1_CBS_all_data_sp_scale.csv。 2. 长白山样地内冠层结构、微气候与秋季物候指标间的关联关系，相关数据集包括SupplementaryData.pickle、ED2_CBS_Canopy.csv、ED2_CBS_Microclimate.csv与ED2_CBSData.csv。 3. 美国国家生态观测站网络（National Ecological Observatory Network, NEON）5个研究样地内按树种分类的秋季物候指标空间变异，相关数据集包括SupplementaryData.pickle与Datas2_DataSpecies.csv。 4. NEON 5个研究样地内冠层结构与模拟微气候指标的分布特征，相关数据集包括SupplementaryData.pickle与Datas3_DataYear.csv。 5. 模拟微气候因子的精度评估，相关数据集包括SupplementaryData.pickle、Datas4_allData_Meas_Simu.csv、Datas4_GLI_accuracy.csv与Datas4_Tbuffer_accuracy.csv。 6. 展示秋季物候指标对微气候因子响应的偏依赖图，相关数据集包括SupplementaryData.pickle、Datas5_PDP_DataYear.csv、Datas5_partial_dof.csv、Datas5_partial_sof.csv。 7. NEON 5个研究样地内冠层结构通过调控微气候条件进而影响秋季物候的作用路径，相关数据集包括Datas6_SEMData.csv。 8. NEON 5个研究样地内不同高度模拟的微气候因子与秋季物候指标间的关联关系，相关数据集包括SupplementaryData.pickle、Datas7_AllData_heights.csv与Datas7_DiffHeights.csv。 9. NEON 5个研究样地2019年秋季起始日（Start of Autumn, SOA，代码中亦以SOF（start of fall）指代）与秋季持续时长（Duration of Autumn, DOA，代码中亦以DOF（duration of fall）指代）间的相关性，相关数据集包括SupplementaryData.pickle与Datas8_SOFDOF_DataYear.csv。 10. 2019年NEON 5个研究样地内微气候因子与秋季结束日（End of Autumn, EOA，代码中亦以EOF（end of fall）指代）间的关联关系，相关数据集包括SupplementaryData.pickle、Datas9_MiroEOF_DataYear.csv与Datas9_Micro_Lmm.csv。 11. 随秋季物候转化进程变化的微气候条件时间动态，相关数据集包括SupplementaryData.pickle与Datas10_all_micro_time.csv。 12. 本研究所有统计分析所用的相似海拔区域筛选，相关数据集包括SupplementaryData.pickle与Datas12_DTMData.csv。 Maps文件夹包含NEON 5个研究样地2018-2022年的全部秋季物候数据（秋季起始日SOA、秋季持续时长DOA与秋季结束日EOA），以及2020年长白山样地的对应物候数据。 注：代码中使用的SOF、DOF与EOF分别与SOA、DOA及EOA含义一致。