# Vegetation growth responses to climate change: a cross-scale analysis of biological memory and time-lags using tree ring and satellite data The dataset includes tree-ring data for individual trees across three species, encompassing dimensionless tree-ring width (TRW) measurements, as well as data on the enhanced vegetation index (EVI), leaf area index (LAI), gross primary productivity (GPP), and various climate parameters. The TRW serves as an indicator of radial stem growth at the tree-species level. Remote sensing-based data of EVI, LAI and GPP were used to monitor ecosystem-scale canopy dynamics, leaf growth, and ecosystem carbon sequestration capacity, respectively. ## Description of the data and file structure 1. Climate_1956_2017.csv: The dataset includes the mean air temperature, mean maximum air temperature, mean minimum air temperature, mean sunshine duration, and total precipitation from 1956 to 2017 on a daily basis in the study area. *Notes*: Lat, Latitude; Lon, longitude; Elev, Elevation; MTEM, mean air temperature (ºC); MaxTEM, mean maximum air temperature (ºC); MinTEM, mean maximum air temperature (ºC); X20to20PRE, accumulated precipitation at 20-20 (mm); SSD, mean sunshine duration (h). 2. TRW_LF.csv: This dataset comprises data for each core of individual trees belonging to the Liquidambar formosana (LF), coded as LF_01A, where 'LF' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 3. TRW_CE.csv: This dataset comprises data for each core of individual trees belonging to the Castanopsis eyrei (CE), coded as CE_01A, where 'CE' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 4. TRW_CH.csv: This dataset comprises data for each core of individual trees belonging to the Castanea henryi (CH), coded as CH_01A, where 'CH' denotes the tree species, '01' represents the tree number, and 'A' indicates the core sample number taken from each tree. The units for this tree-ring data are in 0.001mm. 5. Dimensionless_TRW_data_of_the_three_tree_species.csv: Between October 2020 and July 2022, we sampled 25-29 mature and healthy trees per species, collecting one-to-two cores from each tree at 1.3 m above the ground using a 5.15 mm increment borer. The tree-ring cores were fixed, dried, polished, and visually cross-dated under a binocular microscope. We measured tree-ring width with the LINTAB™ 6 system to a 0.01-mm accuracy, covering data from 1957 to 2017. Standardization of tree-ring width data involved two phases. First, COFECHA software ensured the quality of cross-dating results by evaluating the synchronization of growth patterns across samples. Next, we used the detrend function from the dplR package in R to fit a modified negative exponential curve to each raw tree-ring series for detrending. Standardized indices were calculated by dividing the original ring widths by the fitted values and combining them into a single standardized chronology using a bi-weight robust mean to mitigate outlier influence. *Notes*: CE, Castanopsis eyrei; CH, Castanea henryi; LF, Liquidambar formosana. 6. EVI_MOD13Q1_16days.csv: The dataset consists of the enhanced vegetation index (EVI) for the study area, measured over 16-day periods. *Notes*: Start, date of start; End, date of start; EVI, enhanced vegetation index (unitless). 7. LAI_MCD15A2H_16days.csv: The dataset consists of the leaf area index (LAI) for the study area, measured over 16-day periods. To ensure a consistent time resolution for remote sensing-based vegetation indicators, the 8-day time periods of LAI was aligned with the 16-day time periods of EVI. This alignment was achieved by averaging LAI values from two consecutive 8-day periods. *Notes*: Start, date of start; End, date of start; LAI, leaf area index (m2/m2). 8. GPP_MOD17A2H_16days.csv: The dataset consists of the gross primary productivity (GPP) for the study area, measured over 16-day periods. To ensure a consistent time resolution for remote sensing-based vegetation indicators, the 8-day time periods of GPP was aligned with the 16-day time periods of EVI. This alignment was achieved by calculating GPP as the cumulative value of two consecutive 8-day periods. *Notes*: Start, date of start; End, date of start; GPP, gross primary productivity (kg C/m2).

Vegetation growth is affected by past growth rates and climate variability. However, the impacts of vegetation growth carryover (VGC; biotic) and lagged climatic effects (LCE; abiotic) on tree stem radial growth may be decoupled from photosynthetic capacity, as higher photosynthesis does not always translate into greater growth. To assess the interaction of tree-species level VGC and LCE with ecosystem-scale photosynthetic processes, we utilized tree-ring width (TRW) data for three tree species: Castanopsis eyrei (CE), Castanea henryi (CH, Chinese chinquapin), and Liquidambar formosana (LF, Chinese sweet gum), along with satellite-based data on canopy greenness (EVI, enhanced vegetation index), leaf area index (LAI), and gross primary productivity (GPP). We used vector autoregressive models, impulse response functions, and forecast error variance decomposition to analyze the duration, intensity, and drivers of VGC and of LCE response to precipitation, temperature, and sunshine duration. The results showed that at the tree-species level, VGC in TRW was strongest in the first year, with an average 77% reduction in response intensity by the fourth year. VGC and LCE exhibited species-specific patterns; compared to CE and CH (diffuse-porous species), LF (ring-porous species) exhibited stronger VGC but weaker LCE. For photosynthetic capacity at the ecosystem scale (EVI, LAI, and GPP), VGC and LCE occurred within 96 days. Our study demonstrates that VGC effects play a dominant role in vegetation function and productivity, and that vegetation responses to previous growth states are decoupled from climatic variability. Additionally, we discovered the possibility for tree-ring growth to be decoupled from canopy condition. Investigating VGC and LCE of multiple indicators of vegetation growth at multiple scales has the potential to improve the accuracy of terrestrial global change models.

The dataset includes tree-ring data for individual trees across three species, encompassing dimensionless tree-ring width (TRW) measurements, as well as data on the enhanced vegetation index (EVI), leaf area index (LAI), gross primary productivity (GPP), and various climate parameters. The TRW serves as an indicator of radial stem growth at the tree-species level. Remote sensing-based data of EVI, LAI and GPP were used to monitor ecosystem-scale canopy dynamics, leaf growth, and ecosystem carbon sequestration capacity, respectively. Dimensionless tree-ring width (TRW) measurements method: Between October 2020 and July 2022, we sampled 25-29 mature and healthy trees per species, collecting one-to-two cores from each tree at 1.3 m above the ground using a 5.15 mm increment borer. The tree-ring cores were fixed, dried, polished, and visually cross-dated under a binocular microscope. We measured tree-ring width with the LINTAB™ 6 system to a 0.01-mm accuracy, covering data from 1957 to 2017. Standardization of tree-ring width data involved two phases. First, COFECHA software ensured the quality of cross-dating results by evaluating the synchronization of growth patterns across samples. Next, we used the detrend function from the dplR package in R to fit a modified negative exponential curve to each raw tree-ring series for detrending. Standardized indices were calculated by dividing the original ring widths by the fitted values and combining them into a single standardized chronology using a bi-weight robust mean to mitigate outlier influence.