Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change

Abstract

Microbes play an essential role in ecosystem functions, including carrying out biogeochemical cycles, but are currently considered a black box in predictive models and all global biodiversity debates. This is due to (i) perceived temporal and spatial variations in microbial communities and (ii) lack of ecological theory explaining how microbes regulate ecosystem functions. Providing evidence of the microbial regulation of biogeochemical cycles is key for predicting ecosystem functions, including greenhouse gas fluxes, under current and future climate scenarios. Using functional measures, stable-isotope probing, and molecular methods, we show that microbial (community diversity and function) response to land use change is stable over time. We investigated the change in net methane flux and associated microbial communities due to afforestation of bog, grassland, and moorland. Afforestation resulted in the stable and consistent enhancement in sink of atmospheric methane at all sites. This change in function was linked to a niche-specific separation of microbial communities (methanotrophs). The results suggest that ecological theories developed for macroecology may explain the microbial regulation of the methane cycle. Our findings provide support for the explicit consideration of microbial data in ecosystem/climate models to improve predictions of biogeochemical cycles.

Fig 1

(A to D) Seasonal net CH₄-C fluxes from soils at Bad à Cheo (A), Glensaugh (B), Craggan (C), and Tulchan (D); (E) yearly CH₄ flux estimates from the nonforested and forested habitats. A positive value means that a production of CH₄ occurs, whereas a negative flux denotes a sink of CH₄. (A to D) The curves represent the flux means (error bars are standard errors of the means [SEM]; n = 4) of each season for each habitat from the closed-chamber experiment. Significance was analyzed through a nested ANOVA (land use by season). (E) Each histogram represents the yearly net CH₄ flux average ± the SEM (n = 4 replicates) based on the upscaling of seasonal net CH₄-C flux measurements. Data from the four sampling sites (Bad à Cheo, Glensaugh, Craggan, and Tulchan) which had similar land uses (bog, grassland, heathland, pine forest, or birch forest) were combined during upscaling. The method used for upscaling can be found within the text. For each site, statistical differences between seasons within each habitat are indicated by different Roman letters (a, b), while Greek letters (α, β, γ) indicate statistical differences between land uses according to multiple pairwise comparisons (α = 0.05).

Fig 2

Relationship between CH₄ flux and methanotrophic community structure at Bad à Cheo (A), Glensaugh (B), Craggan (C), and Tulchan (D). The empty shapes (IPCA versus IPCA) are data points representing the IPC scores after analysis of the pmoA-based T-RFLP profiles with the AMMI model. The filled shapes (IPCA versus CH₄ flux) are data points corresponding to the strongest linear regression of the net seasonal CH₄ fluxes (Fig. 1A to D) with one of the IPC scores. The data points within each habitat represent the averages over replicates (n = 4) of the IPC scores of each season.

Fig 3

Community analysis of type II methanotrophs and related organisms using the pmoA microarray (n = 44). Within each habitat, each row represents a replicate. The results were normalized first to the positive-control probe mtrof173 and then to the reference values determined individually for each probe (29). Color coding is as follows: red color indicates the maximum achievable signal for an individual probe, while blue color indicates that no detectable PCR product hybridized to that probe. The color gradient between blue and red reflects the proportion of hybridization.

Fig 4

Relationships between methanotroph diversity and changes in CH₄ flux associated with land use treatment (n = 4 replicates). (A) The methanotroph richness (or OTU richness) was calculated as the square root transformation of the number of T-RFs present in each sample (among the 15 most abundant T-RFs of the T-RFLP profiles, which constituted >94% coverage). (B) Proportions of USCα and cluster 5 microorganisms were calculated as the angular transformation (arcsine of the square root) of the ratio of the relative abundance of the T-RFs specific to Methylocapsa sp. (USCα/cluster 5; T-RFs Hha-32 and Hha-129) to the sum of the T-RFs specific to USCα/cluster 5 and the Methylocystaceae family (T-RF Hha-81). Refer to Table 1 for T-RF reference values.