Intra-annual density fluctuations in tree rings are proxies of air temperature across Europe

Higher latewood to earlywood ratio increases resistance of radial growth to severe droughts in larch

As drought has caused great losses of tree growth across the world, the mechanism of how trees adapt to drought has been extensively investigated. However, how trees change their late- to earlywood ratio (LER) to adapt to severe drought events remains poorly understood. We used a network of Larix principis-rupprechtii earlywood and latewood width data from 1979 to 2018, covering most of the distribution of planted larch across North China, to investigate how latewood proportion affected trees' resistance to drought. The interactions among LER, minimum temperature, vapor pressure deficit (VPD), growing season length, and their contributions to drought resistant (Rt) were estimated using structural equation models. The results show a significant increase in LER of the juvenile wood throughout the first 15 growth rings after which it stabilizes. The LER decreased significantly with elevation for the juvenile wood. March-May temperature and VPD were the main determinant in the LER of mature wood. The sensitivity of radial growth to droughts was positively changed with LER when LER was below 0.50, but negatively changed with LER when LER is above 0.50. We confirmed that high LER increases resistance of tree growth to severe droughts in L. principis-rupprechtii. Our results highlight that a higher proportion of latewood is formed in dry years, and this high drought sensitivity of LER in turn led to an increased resistance to drought. This combination of reduced radial growth during dry years, while the latewood proportion remains increases maybe an adaptive strategy of larch trees to cope with severe droughts.

Impact of intra-annual wood density fluctuation on tree hydraulic function: insights from a continuous monitoring approach

Climate change significantly impacts global forests, leading to tree decline and dieback. To cope with climate change, trees develop several functional traits, such as intra-annual density fluctuations (IADFs) in tree rings. The formation of these traits facilitates trees to optimize resource allocation, allowing them to withstand periods of stress and eventually recover when the conditions become favourable again. This study focuses on a Pinus pinaster Aiton forest in a warm, drought-prone Mediterranean area, comparing two growing seasons with different weather patterns. The innovative continuous monitoring approach used in this study combines high-resolution monitoring of sap flow (SF), analysis of xylogenesis and quantitative wood anatomy. Our results revealed the high plasticity of P. pinaster in water use and wood formation, shedding light on the link between IADFs and tree conductance. Indeed, the capacity to form large cells in autumn (as IADFs) improves the total xylem hydraulic conductivity of this species. For the first time, a continuous SF measurement system captured the dynamics of bimodal SF during the 2022 growing season in conjunction with the bimodal growth pattern observed through xylogenesis monitoring. These results highlight the intricate interplay between environmental conditions, water use, wood formation and tree physiology, providing valuable insights into the acclimation mechanisms employed by P. pinaster to cope with weather fluctuations.

INTRAGRO: A machine learning approach to predict future growth of trees under climate change

The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra-annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra-annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra-annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid-elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra-annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.