Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France

Abstract. Wood density is a key parameter for estimating forest biomass and carbon stocks. However, the magnitude and the drivers of wood density variation in temperate forests, and the implications of this variation for biomass and carbon assessments, are not well understood. This study provides a comprehensive analysis of wood density variation in trees of western temperate Europe and evaluates its impacts on forest aboveground biomass (AGB) estimates at multiple spatial scales. From an initial dataset comprising wood density measurements from 110,763 individual trees, representing 156 species across mainland France, we analysed a subset of 44 species accounting for 97 % of the growing stock and providing sufficient observations for modelling. We developed linear models of wood density based on tree, stand, site, and climatic variables, and successively examined the contributions of taxonomic identity, environmental factors and their interactions. We also constructed a model using variables potentially accessible through spatial layers (i.e., GIS-based data) at broad scales and fine resolutions, to assess their predictive capacity. Models were applied to French National Forest Inventory (NFI) data to estimate aboveground biomass (AGB) across four spatial scales: the national level, biogeographical regions and subregions (both delineated through biophysical partitioning of the territory) and individual NFI plots. Our analysis revealed that variation in wood density stemmed primarily from interspecific differences (78.5 % of the total variance), with the remaining 21.5 % attributable to intraspecific variability. Our best performing model—combining variables on species identity, tree dimensions, stand structure, site conditions and climate—explained 82 % of total wood density variation, though it captured only a modest portion of intraspecific variability, found mainly driven by tree dimensions and mean annual temperature. In contrast, the model relying solely on environmental factors and the one based on GIS-layer variables accounted for 14 % and 34 % of the variation, respectively. While accounting for wood density variation had minimal impact on national-scale AGB estimates, it caused deviations of up to 30 % at finer scales, such as biogeographical subregions and individual NFI plots. These findings highlight the importance of incorporating wood density variation into forest biomass and carbon assessment, especially at regional to local scales. Given its dominant role, we recommend integrating species identity as much as possible to enhance the accuracy of forest biomass and carbon stock assessment across spatial scales.