Vaisanen, M. | bioRxiv Science

bioRxiv2026

Soil microbiome structure and function reflect environmental variation rather than reindeer presence in a northern peatland

Northern peatlands store large carbon stocks but are sensitive to disturbance. Hydrology, vegetation, herbivory and snow conditions may affect the soil microorganisms driving methane (CH) and nitrous oxide (N2O) cycling. We investigated how reindeer exclusion and snow depth (increased and reduced relative to ambient) manipulations (ongoing for three seasons) influenced archaeal and bacterial communities in a boreal rich fen. Metagenomic (MG) and metatranscriptomic (MT) sequencing were combined with pore-water chemistry and CH flux measurements to link the microbiome to ecosystem processes. Microbial communities differed between outside and inside the exclosure. However, these patterns primarily reflected underlying hydrological variation. Slightly wetter inside plots showed higher expression of denitrification genes (norB, nosZ) and lower (nirS+nirK)/nosZ ratios, indicating greater potential for complete denitrification to N2 instead of N2O. Methane dynamics were mainly associated with vegetation: plots associated with Carex rostrata exhibited lower pmoA/mcrA ratios and elevated CH fluxes. Snow manipulations had subtle effects: reduced snow depth decreased the expression of taxa dependent on microbial interactions, while the effect to the investigated metabolic marker genes was small. Overall hydrology, leading to variations in redox conditions and nutrient availability, together with vegetation appeared as the primary drivers on microbial greenhouse gas processes in this peatland.

microbiology↗

bioRxiv2023

Controlling biases in targeted plant removal experiments

O_LITargeted removal experiments are a powerful tool to assess the effects of plant species or (functional) groups on ecosystem functions. However, removing plant biomass in itself can bias the observed responses. This bias is commonly addressed by waiting until ecosystem recovery, but this is inherently based on unverified proxies or anecdotal evidence. Statistical control methods are efficient, but restricted in scope by underlying assumptions. C_LIO_LIWe propose accounting for such biases within the experimental design, using a gradient of biomass removal controls. We demonstrate the relevance of this design by presenting i) conceptual examples of suspected biases and ii) how to observe and control for these biases. C_LIO_LIUsing data from a mycorrhizal association-based removal experiment we show that ignoring biomass removal biases (including by assuming ecosystem recovery) can lead to incorrect, or even contrary conclusions (e.g., false positive and false negative). Our gradient design can prevent such incorrect interpretations, whether aboveground biomass has fully recovered or not. C_LIO_LIOur approach provides more objective and quantitative insights, independently assessed for each variable, than using a proxy to assume ecosystem recovery. Our approach circumvents the strict statistical assumptions of e.g. ANCOVA and thus offers greater flexibility in data analysis. C_LI

ecology↗