| 04 Sep 2025
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.
This manuscript entitled “Wood density variation in European forest species: drivers and implications for multiscale biomass and carbon assessment in France” presents an analysis of wood density variation across temperate forest species in France. The study makes a valuable contribution to understanding wood density variation and its implications for forest biomass estimation. The authors constructed four linear models to identify the biotic and abiotic factors controlling wood density variability. They also applied the NFI-based and GIS-based models to generate a spatial distribution map of wood density across France. Finally, they evaluated the influence of wood density on biomass estimation at multiple scales, such as the plot, subregion, and country levels. A particularly interesting conclusion is that the choice of method for inferring wood density depends on the spatial scale of interest.
I have several comments regarding the methods and overall storyline of the study
Specific comments:
First, the results from the taxonomic and NFI-based models highlight the importance of tree species in explaining wood density variation. However, this may be partly due to the limited set of climate variables included. The authors only included two variables, mean annual temperature and precipitation. I think only temperature and precipitation may not fully reflect spatial differences in specific environmental conditions. It would be helpful to clarify why only these two climatic variables were selected. Would the inclusion of additional climatic and soil variables (such as soil nutrient availability, soil pH, or atmospheric humidity) alter the identified drivers?
Second, I think the inclusion of four distinct models seems somewhat redundant. The environmental model, in particular, appears less critical, as it overlaps significantly with the GIS-based model in terms of predictive capacity using abiotic variables. The GIS-based model, which incorporates both biotic and abiotic factors, could sufficiently illustrate the predictive power of remotely accessible variables (including environmental variables) without the need for a separate environmental model.
Third, given the hierarchical nature of the data (e.g., trees nested within species and plots), the use of ANOVA is not optimal. A generalized linear mixed-effects model (GLMM) would be more appropriate, as it allows variance to be partitioned into random effects (e.g., tree, species, plot) rather than attributing it entirely to fixed effects (e.g., genus, DBH, height). A GLMM would enable all density samples to be analyzed collectively, with nested random effects (e.g., trees within plot and species), thereby improving degrees of freedom and interpretive power.
Furthermore, I like this part of “Difference in forest aboveground biomass (AGB) stock depending on the method used to predict wood density”, and I additionally suggest extending this analysis to more explicitly quantify the total carbon stock (e.g., in PgC of aboveground carbon) and assess potential over- or underestimation at broad scales under different wood density inference methods.
Finally, it remains unclear how the NFI-based model, which relies on species identity, can be upscaled to generate a large-scale wood density map. Species-level information is often unavailable at broad scales, and the model's dependency on such data may limit its practical application. The authors should clarify how this limitation is addressed or propose alternative approaches for spatial extrapolation.
A more detailed description of the candidate predictor variables is needed. For instance, what is the spatial resolution of the remote sensing data used in the GIS-based model? Which specific spectral indices were selected, and from which satellite products were they derived?