Forests are crucial carbon sinks, and their soil microbiomes play a significant role in carbon cycling. Soil microbes mediate soil respiration, litter decomposition, and tree growth. While tree species composition is known to impact forest processes, understanding the impact of soil microbial community composition on forest-scale processes is urgently needed. Experimental and observational studies suggest that microbial composition affects forest functioning by influencing carbon pools, fluxes, and process efficiencies. For instance, dark septate fungal endophytes stimulate plant growth, as do various other bacteria and mycorrhizal fungi. However, the direct link between soil microbial composition and total forest carbon storage remains unclear. This study aims to investigate this relationship using a large-scale dataset across Europe, bridging the gap between microbiome diversity and in situ forest carbon accumulation and storage observations.

Existing research highlights the influence of microbial diversity on soil respiration, microbial carbon use efficiency, and decomposition rates. Studies have linked ectomycorrhizal fungal composition to tree growth variations. Variation in decomposition rates has been connected to bacterial composition and fungal richness, suggesting links to soil organic carbon storage. However, how these factors translate into total forest carbon storage is unknown. The relationship between plant biomass and soil organic carbon storage is complex, influenced by priming effects, mineralogy, forest management, disturbances, soil carbon saturation, and mycorrhizal symbiosis. A meta-analysis revealed a negative correlation between the positive effects of elevated CO2 on plant growth and changes in soil organic carbon stocks, attributed to nutrient scavenging by ectomycorrhizal fungi. The context-dependency of this relationship necessitates an explicit examination of both above and belowground carbon pools in relation to forest soil microbiomes.

This study utilized data from 238 forest monitoring plots across 15 European countries within the ICP Forests network. Soil samples were collected from organic and mineral horizons, and DNA sequencing was used to generate soil microbiome profiles of bacteria and fungi. Extensive data on forest carbon cycling and storage was available for each plot. Tree growth was calculated using periodic diameter at breast height (DBH) measurements and allometric equations, with tree biomass carbon stocks estimated assuming 50% carbon content. Soil carbon and nitrogen stocks were calculated from elemental content, bulk density, and sampling depth. Soil pH and clay content were also measured. Mean annual temperature and precipitation were obtained from WorldClim, and nitrogen deposition predictions from EMEP. DNA was extracted from soil samples and used to amplify the 16S rRNA gene (for bacteria) and the ITS region (for fungi). Sequences were processed using QIIME2, OTUs were clustered, and taxonomy and functional guild annotations were assigned. Statistical analyses included distance-based redundancy analysis to correlate microbiome composition with environmental variables and generalized additive models to predict tree growth, tree biomass, and soil carbon stocks.

Fungal, but not bacterial, community composition was strongly correlated with forest biotic variables, including dominant tree type, forest age, and tree growth rate. Fungal composition, particularly PCoA axis 1, was correlated with tree biomass and tree growth rates, even after accounting for other environmental factors. Fungal richness was also correlated with tree growth, with the strongest links observed in conifer forests and the mineral soil horizon. Ectomycorrhizal and endophytic fungi were most strongly linked to tree growth. Fungal endophyte richness was positively and strongly correlated with tree growth rates. Both fungal and bacterial community compositions were correlated with organic horizon carbon stocks, but the correlation was stronger for bacteria. Fungal and bacterial richness were negatively correlated with organic horizon carbon stocks, while bacterial composition was also correlated with mineral horizon carbon stocks. Analysis of indicator species revealed various ectomycorrhizal and endophytic fungal OTUs linked to tree growth and organic horizon carbon stocks.

The findings highlight the importance of fungal community composition, particularly ectomycorrhizal and endophytic fungi, as a strong predictor of forest carbon storage. The stronger link between fungal composition and tree growth compared to bacterial composition may be due to the prevalence of biotrophic fungal groups in forests. The positive correlation between fungal endophyte richness and tree growth is a notable finding, suggesting their potential role in enhancing tree growth and consequently, carbon sequestration. The negative correlation between microbial richness and organic horizon carbon stocks could be due to increased decomposition rates associated with higher diversity. However, bacterial composition showed a stronger link to carbon stocks than richness, indicating that specific bacterial taxa may have a more significant role in carbon cycling than overall diversity. The study suggests that the soil mycobiome is a valuable indicator of forest carbon storage.

This study demonstrates a strong link between fungal community composition and forest carbon storage across Europe. Fungal endophytes are highlighted as a key driver of tree growth, emphasizing their potential for enhancing carbon sequestration. Further research is needed to investigate the causal relationships between microbial communities and carbon cycling, and to explore the potential of manipulating fungal communities to improve carbon storage in forests. The methods utilized here can be applied to other forest ecosystems globally.

The observational nature of the study limits the ability to establish causal relationships. The use of DNA sequencing to study the microbiome does not provide complete information about microbial activity or functional roles. The influence of specific tree species was not explicitly analyzed, and the trophic strategies of some fungi were difficult to fully resolve. While the SoilGrids data provided good estimates of soil properties, relying on them for some analyses introduces some degree of uncertainty.