| 23 Apr 2026
Tree-microbe-soil interactions affecting soil organic carbon fractions in Mediterranean forest soils
Abstract. Soil organic carbon (SOC) represents a major terrestrial carbon pool, yet the processes that regulate its storage remain uncertain, particularly in water-limited ecosystems. The behavior of SOC is informed by partitioning into mineral-associated organic carbon (MAOC), considered more persistent, and particulate organic carbon (POC), which is more labile. We investigated how SOC fractions were affected by forest tree species, including Pinus halepensis (a canopy conifer), Quercus calliprinos (a sub-canopy broadleaf), and Pistacia lentiscus (an understory woody shrub) as compared to mixing of these species, focusing on shallow-soil mature Mediterranean stands. To elucidate further insights, the effects of soil physicochemical properties and microbial community were examined. Across soil samples, SOC concentrations were up to twofold higher under tree canopies compared to forest gaps with Quercus plots storing 10–30% more SOC than Pinus and Pistacia plots. SOC variation was primarily explained by POC, for which mixed plots showed increased concentrations as compared to monospecific plots. In contrast, MAOC displayed a saturation pattern (maximum ~45 g C kg⁻¹ soil), strongly constrained by clay and silt content, with apparent high saturation levels. Mixed forests supported seasonally stable microbial communities but did not consistently increase microbial diversity. Bacterial composition was shaped by microsite conditions, with soils under tree canopies harboring subsets of the more diverse forest-gap communities. Overall, despite the fact that mixed forest increased microbial richness, this effect did not propagate to affect the different soil C pools. Nevertheless, the effect of forest type on soil C pools was modulated by specific microsites and tree-species characteristics. For instance, transitioning to mixed forests could increase SOC by approximately 6.1 Mg C ha⁻¹ compared to monospecific pine forests, but this carbon is expected to primarily be stored in the labile POC pool, especially in soils near saturation.
This study collected soil samples from different forests (or forest stands), measured soil organic carbon and its fractions as well as related soil properties and analyzed the relationships between soil carbon fractions and soil organic carbon storage, together with the factors influencing soil carbon fractions. Overall, the study provides comprehensive data, employs appropriate analytical methods, and presents clear descriptions across all sections. The manuscript is generally well written and clearly organized. I have no major concerns regarding this manuscript, but only some specific minor comments for the authors to consider.
L26-49: The title of this manuscript is “Tree–microbe–soil interactions,” but the description of these interactions in the abstract is insufficient. It is suggested that the authors strengthen this aspect in the abstract.
L42-44: The abstract should focus on how environmental and microbial factors influence soil organic carbon and its fractions, rather than overemphasizing other indicators such as microbial diversity.
L146-148: This is not a complete hypothesis because the underlying mechanism is not stated. In addition, forest gaps receive relatively lower plant carbon inputs and therefore decomposition processes are expected to dominate. Since POC fractions are more easily decomposed, it can be hypothesized that the proportion of MAOC may be higher.
L157-158: What is the mean annual temperature? This is a more important indicator. Please provide it.
L217-219: It is usually not appropriate to define particles >50 μm as sand and those <50 μm as silt plus clay, because different soil classification systems use different thresholds (for example, in some systems particles >20 μm are defined as sand). It is recommended to instead define particles >50 μm as POM and those <50 μm as MAOM.
L378-528: The Results section is very detailed, but overly so. For example, the description of the structural equation modeling results, which was divided into four subsections and is unnecessary. The authors are encouraged to appropriately condense the text and focus on reporting the most important and central findings.
Figure 3b: It is not clear what the purpose of including the regression line of Díaz-Martínez et al. (2024) is in this context. Please provide an explanation. Also, why is the R² value negative? Please also provide an explanation.
Figure 3c: Is it reasonable for MAOC saturation to exceed 100%? Please verify.
Figure 4a: A linear mixed model for individual variables is acceptable, but it is not sufficiently comprehensive, because significance (*, **, ***) and R² alone cannot identify which factors are the most important in controlling soil carbon fraction storage. The authors are encouraged to further analyze the relative importance of predictors. In addition, the meanings of many indicators on the x-axis are unclear. For example, what do Temperature, Phosphorus, and Magnesium specifically refer to? Are they total nutrients or available nutrients? It would be better if these were clearly specified directly in the figure.
Figure 6: The structural equation modeling (SEM) lacks some connections among variables, such as paths from aboveground biomass and microbial communities to POC and/or MAOC. In addition, “Above ground biomass” should be changed to “Aboveground biomass”. What does “Soil properties” refer to? This cannot be determined from the figure. Moreover, does soil silt and clay content not belong to soil properties?