DOI: 10.5061/dryad.3tx95x6qd
title: Data from: Vegetation growth responses to climate change: A cross-scale analysis of biological memory and time-lags using tree ring and satellite data

description: 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.
description: 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.
 <strong>Dimensionless tree-ring width (TRW) measurements
 method:</strong> 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.
description: # 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).