1. Temperature and photoperiod interactions influence the cessation of wood growth in three temperate and boreal conifersJianhong Lin, Cyrille Rathgeber, Patrick Fonti, Sergio Rossi, Henri E. Cuny, Edurne Martinez Del Castillo, Katarina Čufar, Jesús J. Camarero, Alessio Giovannelli, Harri Mäkinen, Peter Prislan, Walter Oberhuber, Hanuš Vavrčík, Jianguo Huang, Andreas Gruber, Vladimir Gryc, Václav Treml, Martin De Luis, Jožica Gričar, Nicolas Delpierre, 2026, izvirni znanstveni članek Povzetek: Cambium phenology is a crucial process in wood production and carbon sequestration of forest ecosystems. Although cambium phenology has been widely studied, research specifically focusing on the cessation of wood formation remains limited. To better understand the influence of environmental and intrinsic factors on the cessation of wood formation, we built and compared three ecophysiological models (temperature sum model, photoperiod-influenced temperature sum model and soil moisture- and photoperiod-influenced temperature sum model) in their ability to predict the date of cessation of xylem cell enlargement (cE) in three major Northern Hemisphere conifer species (Black spruce, Norway spruce and Scots pine). We developed these models based on xylogenesis data collected for 130 site‐years across Europe and Canada. Our results demonstrate that the photoperiod-influenced temperature sum model is well-supported by data across all conifer species, with a RMSE of 9.2 days, suggesting that both temperature and photoperiod are critical drivers of wood growth cessation. However, incorporating soil moisture effects does not improve model performance. Our model effectively captures the inter-site variability in cE across a wide environmental gradient, with a fair model efficiency (ME = 0.51 ± 0.22), but performed less well for annual anomalies (ME = 0.10 ± 0.09). Additionally, we found that the total ring cell number also affected prediction accuracy. Using this model, we reconstructed historical trends in cE over the past six decades and found a trend to delayed cessation dates. This delay varied geographically, with slower shifts at higher latitudes and elevations, likely due to constrained cambial responses and conservative growth strategies in colder regions. Our model framework offers a simple yet accurate approach for predicting wood growth cessation at large spatial scales, providing a basis for integrating cambium phenology into land surface models and forest productivity assessments.
Ključne besede: cambium phenology, ecophysiological models, xylem formation, climate change, global warming, northern hemisphere forests
Objavljeno v DiRROS: 12.02.2026; Ogledov: 567; Prenosov: 260
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2. Modelling seasonal dynamics of secondary growth in RJernej Jevšenak, Jožica Gričar, Sergio Rossi, Peter Prislan, 2022, izvirni znanstveni članek Povzetek: The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.
Ključne besede: artificial neural networks, cambium, generalized additive model, Gompertz function, growing season, intra-annual time series
Objavljeno v DiRROS: 21.07.2022; Ogledov: 1686; Prenosov: 1181
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