Kobra Maleki
Research Scientist
(+47) 465 06 548
kobra.maleki@nibio.no
Place
Steinkjer
Visiting address
Ogndalsveien 2, 7713 Steinkjer
Abstract
Process-based forest models are increasingly used to guide management, but few are validated against fine-scale spatial patterns that emerge from neighborhood interactions. We tested whether the spatially explicit individual-based model SORTIE-ND, which simulates growth, mortality, and recruitment as functions of neighbourhood interactions among individual trees, can reproduce observed fine-scale structure in boreal mixedwoods. Using long-term data from the Lake Duparquet Research and Teaching Forest (Québec) station, we initialized simulations from transect plots representing younger post-fire stands and compared simulated outcomes to independent hectare plots of similar ages along a 249-year chronosequence. The spatial structure was quantified with inhomogeneous l-functions for univariate and bivariate patterns, and model performance was assessed by comparing observed curves to simulation envelopes. SORTIE-ND reproduced fine-scale patterns for balsam fir and trembling aspen, showed partial agreement for white spruce, and failed to match the observed clustering of paper birch. Cross-species patterns were captured for fir–aspen but not for pairs involving white spruce. These results indicate that SORTIE-ND can approximate fine-scale spatial patterns for dominant species in boreal mixedwoods, but limitations remain where key processes (e.g., vegetative propagation, substrate dependence) are under-represented. We discuss implications for stand- to landscape-scale management and recommend model extensions and more independent validation to improve generality.
Abstract
Data from the Norwegian national forest inventory spanning from 1994 to 2022 were analyzed to explore the growth dynamics of pure and mixed stands of Norway spruce and Scots pine. The derived large dataset enabled the development of models designed to assess how stand characteristics and drought interactively affect volume increment at the stand and individual tree level. The analysis revealed that pine-dominated stands outperform spruce-dominated stands at lower site qualities, while the opposite was true at higher site qualities. Mixed stands exhibited overyielding, with productivity exceeding the expected combined pure stand productivity of the individual species components. Based on model predictions, relative overyielding increased with stand age and declined with increasing site quality. Transgressive overyielding, where mixed stands outperform pure stands of either species, was predicted for medium site qualities. Drought-induced productivity losses increased with spruce proportion, especially at lower site qualities, and with stand density. The presence of pine in mixed stands mitigated the negative effects of drought on spruce. The findings of this study suggest that pure spruce stands should be avoided on lower-quality sites while mixed stands with appropriate thinning interventions should be promoted to maintain productivity under changing climatic conditions.
Abstract
No abstract has been registered
Division of Biotechnology and Plant Health
FORESIGHT: Forest opportunities, risks and ecosystem services in a changing climate in Norway
Forest ecosystems are increasingly under pressure from climate change, emerging pests and pathogens, and more frequent extreme weather events. When such disturbances occur simultaneously—or interact with one another—the risk of severe damage can increase substantially. The FORESIGHT project aims to understand these complex, interacting challenges and to translate this knowledge into practical applications.
Division of Forest and Forest Resources
A Decision Support System for emerging forest management alternatives
This project aims to develop advanced tree growth models using LiDAR-derived, high-density point cloud data to improve the simulation of forest dynamics under close-to-nature silvicultural practices. By modeling tree-level growth in structurally complex and heterogeneous stands, these models will support more accurate, spatially explicit forest simulations and inform sustainable and diversified forest management decisions.