Marta Vergarechea
Research Scientist
Biography
I am a forest researcher working at the interface of forest ecology, modelling, and decision support. I hold a B.Sc. and M.Sc. in Forest Engineering from the University of Valladolid and a Ph.D. in Conservation and Sustainable Use of Forest Systems from INIA-CIFOR, where my research focused on mixed forest dynamics and climate–growth relationships.
My current work focuses on forest modelling, machine-learning approaches for growth and mortality prediction, and optimization tools for forest management planning, with a particular focus on ecosystem services. I work extensively with National Forest Inventory data, dendrochronological information, and forest simulation frameworks to analyse forest dynamics and evaluate trade-offs among multiple objectives under different management and climate scenarios.
My research aims to strengthen the link between advanced modelling methods and practical decision-support tools for multifunctional and climate-resilient forest management.
Authors
Marta Vergarechea C Antón-Fernández J.U Jepsen Ole Petter Laksforsmo Vindstad Nicolas Cattaneo J.J Camarero Rasmus AstrupAbstract
resilience. In Norway, birch species (Betula pendula and Betula pubescens) dominate large areas of boreal forest, yet large-scale patterns of their age distribution and growth dynamics remain poorly quantified. Using increment core data from 2818 trees sampled across the Norwegian National Forest Inventory, spanning five vegetation zones (58–71◦N) and a broad productivity gradient, we analyzed the drivers of birch age structure and growth variation across age classes and historical cohorts. Intermediate-aged trees (35–80 years) dominated most regions, whereas older individuals were scarce, particularly on productive sites, reflecting the combined effects of forest management and the life-history strategy of fast-growing pioneer species. When compared at equivalent biological ages, younger trees consistently showed higher basal area increment (BAI) than older trees, with differences strongest during early development and on productive sites. Cohort analyses showed a pronounced long-term increase in juvenile growth: mean BAI during the first ten years after reaching breast height increased steadily across successive cohorts over the past 150 years. This increase became more pronounced after ~1960 and was consistent across vegetation zones and site productivity classes. Although sampling and survivor bias cannot be fully excluded, the consistency across environmental gradients points to broad-scale changes in early growth dynamics of birch forests in Norway. These results underscore the importance of considering both age structure and cohort-related variation when interpreting forest dynamics and planning future management.
Authors
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupAbstract
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Authors
Marta Vergarechea Ignacio Sevillano Arne Steffenrem A. Ahtikoski H. Holmström C. Antón-FernándezAbstract
Balancing wood production, biodiversity, and climate regulation is increasingly challenging for forest management, particularly as societal demands intensify. Despite growing interest in genetic improvement to enhance forest productivity, its implications for multiple ecosystem services (FES) remain poorly quantified. To address this gap, we assessed how genetically improved regeneration material, combined with alternative management regimes, influences FES provision in Norway. Using National Forest Inventory data, a climate-sensitive single-tree simulator, and multi-objective optimization, we projected 100-year outcomes under three strategies: no genetic gain, growth-focused improvement, and combined improvement in growth and wood quality. The strongest responses occurred when genetic improvement targeted both growth and wood quality. Under this scenario, harvest net value increased, ecological hotspot areas expanded, and larger set-aside areas were maintained while meeting national harvest demands. Carbon storage in harvested wood products also increased, whereas carbon sequestration in living biomass showed no consistent trend. Genetic gain reinforced positive interactions between bioenergy and climate regulation but left most other FES relationships broadly unchanged. Varying genetic gain levels produced only minor differences across most indicators, suggesting that long-term outcomes depend more on how improved material is integrated within a flexible management portfolio than on the exact magnitude of genetic gain. Positive responses in the structural biodiversity indicators should be interpreted cautiously, as these proxies capture only a limited subset of ecological dimensions and likely underestimate broader biodiversity trade-offs. Overall, genetically improved material represents a complementary tool for enhancing forest multifunctionality when integrated with adaptive, landscape-scale management.
