Explore the vast range of titles available.

Interannual variability of wood density – an important plant functional trait and environmental proxy – in conifers is poorly understood. We therefore explored the anatomical basis of density. We hypothesized that earlywood density is determined by tracheid size and latewood density by wall dimensions, reflecting their different functional tasks. To determine general patterns of variability, density parameters from 27 species and 349 sites across the Northern Hemisphere were correlated to tree-ring width parameters and local climate. We performed the same analyses with density and width derived from anatomical data comprising two species and eight sites. The contributions of tracheid size and wall dimensions to density were disentangled with sensitivity analyses. Notably, correlations between density and width shifted from negative to positive moving from earlywood to latewood. Temperature responses of density varied intraseasonally in strength and sign. The sensitivity analyses revealed tracheid size as the main determinant of earlywood density, while wall dimensions become more influential for latewood density. Our novel approach of integrating detailed anatomical data with large-scale tree-ring data allowed us to contribute to an improved understanding of interannual variations of conifer growth and to illustrate how conifers balance investments in the competing xylem functions of hydraulics and mechanical support.

Tree-ring width (RW), density, elemental composition, and stable carbon and oxygen isotope (δ 13 C, δ 18 O) are widely used as proxies to assess climate change, ecology, and environmental pollution; however, a specific pretreatment has been needed for each proxy. Here, we developed a method by which each proxy can be measured in the same sample. First, the sample is polished for ring width measurement. After obtaining the ring width data, the sample is cut to form a 1-mm-thick wood plate. The sample is then mounted in a vertical sample holder, and gradually scanned by an X-ray beam. Simultaneously, the count rates of the fluorescent photons of elements (for chemical characterization) and a radiographic grayscale image (for wood density) are obtained, i.e. the density and the element content are obtained. Then, cellulose is isolated from the 1-mm wood plate by removal of lignin, and hemicellulose. After producing this cellulose plate, cellulose subsamples are separated by knife under the microscope for inter-annual and intra-annual stable carbon and oxygen isotope (δ 13 C, δ 18 O) analysis. Based on this method, RW, density, elemental composition, δ 13 C, and δ 18 O can be measured from the same sample, which reduces sample amount and treatment time, and is helpful for multi-proxy comparison and combination research.

To better understand fire regimes and their relation to climate in the seasonal tropical forests of continental Southeast Asia, we developed the first multi‐century tree‐ring based fire history chronology for the region. The chronology included 776 fire scars collected at Bidoup NuiBa National Park (BNNP) in the Central Highlands of Vietnam and spans the period 1636–2020. Fires were recorded in 116 years, representing 47% of the years covered by the 249‐year period between the first fire scar (1772) and the last (2020). While only 9% of years within the sampled BNNP forests experienced fires before 1905, 70% recorded fires between 1906 and 1963 and 90% showed evidence of fire after 1963. Fire occurrence was highly correlated with climate indices (wet season Nino 3.4 and dry season regional Palmer Drought Severity Index) during the period 1906–1963, but showed no significant correlation after 1963. Our fire reconstruction from BNNP suggests that the fire regime has shifted from one driven primarily by climate to one in which human activities dominate the occurrence of fire within these seasonal tropical landscapes. Plain Language Summary In many parts of the world fires shape forest structure, composition, and dynamics. While fire regimes and their long‐term impacts on forests have been described for many temperate forests, we know little about the impact of fire in tropical forests. We used tree rings from two tropical conifers (Pinus kesiya and Keteleeria evelyniana) to develop the first multi‐century fire chronology from continental southeast Asia. We used it to reconstruct over 200 years of fire activity in the Central Highlands of Vietnam. We found that fire occurrence in the region was associated with climatic conditions prior to 1963. However, since then an increase in human settlement and activities within these landscapes has led to a massive increase in fire frequency and extent. Our tree‐ring based fire‐history reconstruction shows that the overwhelming pressure of human ignitions have effectively eliminated climate as a factor limiting fires in these landscapes. Key Points First annually resolved, multi‐century fire history reconstruction from monsoon Asia Seasonal and interannual drought conditions have historically been an important driver of fire activity in the region In the 1960s the fire regime shifted from patchy to landscape‐scale occurrence, which was associated with a sudden increase in the regional human population

As climate change drives increased drought in many forested regions, mechanistic understanding of the factors conferring drought tolerance in trees is increasingly important. The dendrochronological record provides a window through which we can understand how tree size and traits shape growth responses to droughts. We analyzed tree-ring records for 12 species in a broadleaf deciduous forest in Virginia (USA) to test hypotheses for how tree height, microenvironment characteristics, and species’ traits shaped drought responses across the three strongest regional droughts over a 60-yr period. Drought tolerance (resistance, recovery, and resilience) decreased with tree height, which was strongly correlated with exposure to higher solar radiation and evaporative demand. The potentially greater rooting volume of larger trees did not confer a resistance advantage, but marginally increased recovery and resilience, in sites with low topographic wetness index. Drought tolerance was greater among species whose leaves lost turgor (wilted) at more negative water potentials and experienced less shrinkage upon desiccation. The tree-ring record reveals that tree height and leaf drought tolerance traits influenced growth responses during and after significant droughts in the meteorological record. As climate change-induced droughts intensify, tall trees with drought-sensitive leaves will be most vulnerable to immediate and longer-term growth reductions.

The long-term relationship between temperature and hydroclimate has remained uncertain due to the short length of instrumental measurements and inconsistent results from climate model simulations. This lack of understanding is particularly critical with regard to projected drought and flood risks. Here we assess warm-season co-variability patterns between temperature and hydroclimate over Europe back to 850 CE using instrumental measurements, tree-ring based reconstructions, and climate model simulations. We find that the temperature-hydroclimate relationship in both the instrumental and reconstructed data turns more positive at lower frequencies, but less so in model simulations, with a dipole emerging between positive (warm and wet) and negative (warm and dry) associations in northern and southern Europe, respectively. Compared to instrumental data, models reveal a more negative co-variability across all timescales, while reconstructions exhibit a more positive co-variability. Despite the observed differences in the temperature-hydroclimate co-variability patterns in instrumental, reconstructed and model simulated data, we find that all data types share relatively similar phase-relationships between temperature and hydroclimate, indicating the common influence of external forcing. The co-variability between temperature and soil moisture in the model simulations is overestimated, implying a possible overestimation of temperature-driven future drought risks.

The normalized difference vegetation index (NDVI) and tree-ring parameters are commonly used indicators in the research on forest ecology and responses to climate change. This paper compiles and analyzes the literature on vegetation index research on the basis of tree-ring information in the past 20 years and provides an overview of the relationship between tree-ring parameters and NDVI, as well as NDVI reconstruction. The research on the vegetation index based on tree-ring data is mainly concentrated in the middle and high latitudes, and relatively few studies are concentrated in the low latitudes. The tree-ring parameters have a strong correlation with the NDVI in the summer. In terms of tree-ring reconstruction NDVI, Sabina przewalskii is the tree with the longest reconstruction sequence so far, and the tree-ring width is the main proxy index. In addition, combining tree rings with the NDVI is useful for assessing forest decline, quantifying the forest response to drought, and monitoring forest productivity. In the future, it is necessary to consider a variety of environmental factors to find the optimal model construction parameters and carry out research on the climate response of forest tree growth and the reconstruction of the historical sequence of the vegetation index at large spatial scales.

Key Message The automatic detection of tree-ring boundaries and other anatomical features using image analysis has progressed substantially over the past decade with advances in machine learning and imagery technology, as well as increasing demands from the dendrochronology community. This paper presents a publicly available dataset of 64 annotated images of transverse sections of commercially grown Pinus taeda L. trees from northern Uruguay, presenting 17 to 24 annual rings. The collection contains several challenging features for automatic ring detection, including illumination and surface preparation variation, fungal infection (blue stains), knot formation, missing bark or interruptions in outer rings, and radial cracking. This dataset can be used to develop and test automatic tree ring detection algorithms. The dataset presented here was used to develop the Cross-Section Tree-Ring Detection (CS-TRD) method, an open-source automated ring-detection algorithm for cross-sectioned images. Dataset access at https://doi.org/10.5281/zenodo.15110647 . Access to the metadata describing the data set:  https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/5fdbd411-9ae1-4ce6-8ef0-cdfa2fbd7a6a .

Aim The International Tree‐Ring Data Bank (ITRDB) is the most comprehensive database of tree growth. To evaluate its usefulness and improve its accessibility to the broad scientific community, we aimed to: (a) quantify its biases, (b) assess how well it represents global forests, (c) develop tools to identify priority areas to improve its representativity, and d) make available the corrected database. Location Worldwide. Time period Contributed datasets between 1974 and 2017. Major taxa studied Trees. Methods We identified and corrected formatting issues in all individual datasets of the ITRDB. We then calculated the representativity of the ITRDB with respect to species, spatial coverage, climatic regions, elevations, need for data update, climatic limitations on growth, vascular plant diversity, and associated animal diversity. We combined these metrics into a global Priority Sampling Index (PSI) to highlight ways to improve ITRDB representativity. Results Our refined dataset provides access to a network of >52 million growth data points worldwide. We found, however, that the database is dominated by trees from forests with low diversity, in semi‐arid climates, coniferous species, and in western North America. Conifers represented 81% of the ITRDB and even in well‐sampled areas, broadleaves were poorly represented. Our PSI stressed the need to increase the database diversity in terms of broadleaf species and identified poorly represented regions that require scientific attention. Great gains will be made by increasing research and data sharing in African, Asian, and South American forests. Main conclusions The extensive data and coverage of the ITRDB show great promise to address macroecological questions. To achieve this, however, we have to overcome the significant gaps in the representativity of the ITRDB. A strategic and organized group effort is required, and we hope the tools and data provided here can guide the efforts to improve this invaluable database.

Dendroclimatology offers the unique opportunity to reconstruct past climate at annual resolution and wood from historical buildings can be used to extend such information back in time up to several millennia. However, the varying and often unclear origin of timbers affects the climate sensitivity of individual tree‐ring samples. Here, we compare tree‐ring width and density of 143 living larch (Larix decidua Mill.) trees at seven sites along an elevational transect from 1400 to 2200 m asl and 99 historical tree‐ring series to parametrize state‐of‐the‐art classification models for the European Alps. To achieve geographical provenance of the historical series, nine different supervised machine learning algorithms are trained and tested in their capability to solve our classification problem. Based on this assessment, we consider a tree‐ring density‐based and a tree‐ring width‐based dataset for model building. For each of these datasets, a general not species‐related model and a larch‐specific model including the cyclic larch budmoth influence are built. From the nine tested machine learning algorithms, Extreme Gradient Boosting showed the best performance. The density‐based models outperform the ring‐width models with the larch‐specific density model reaching the highest skill (f1 score = 0.8). The performance metrics reveal that the larch‐specific density model also performs best within individual sites and particularly in sites above 2000 m asl, which show the highest temperature sensitivities. The application of the specific density model for larch allows the historical series to be assigned with high confidence to a particular elevation within the valley. The procedure can be applied to other provenance studies using multiple tree growth characteristics. The novel approach of building machine learning models based on tree‐ring density features allows to omit a common period between reference and historical data for finding the provenance of relict wood and will therefore help to improve millennium‐length climate reconstructions.

The radiocarbon (14C) calibration curve so far contains annually resolved data only for a short period of time. With accelerator mass spectrometry (AMS) matching the precision of decay counting, it is now possible to efficiently produce large datasets of annual resolution for calibration purposes using small amounts of wood. The radiocarbon intercomparison on single-year tree-ring samples presented here is the first to investigate specifically possible offsets between AMS laboratories at high precision. The results show that AMS laboratories are capable of measuring samples of Holocene age with an accuracy and precision that is comparable or even goes beyond what is possible with decay counting, even though they require a thousand times less wood. It also shows that not all AMS laboratories always produce results that are consistent with their stated uncertainties. The long-term benefits of studies of this kind are more accurate radiocarbon measurements with, in the future, better quantified uncertainties.