Vegetation growth responses to climate change: A cross-scale analysis of biological memory and time-lags using tree ring and satellite data

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.

Methods
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.