Project description:Mixing plant litters of multiple species can alter litter decomposition, a key driver of carbon and nutrient cycling in terrestrial ecosystems. Changes in microbial decomposer communities is proposed as one of the mechanisms explaining this litter-mixture effect, but the underlying mechanism is unclear. In a microcosm litterbag experiment, we found that, at the early stage of decomposition, litter mixing promoted tomato root litter decomposition, thus generating a synergistic nonadditive litter-mixture effect. The transplanting decomposer community experiment showed that changes in microbial decomposer communities contributed to the nonadditive litter-mixture effect on tomato root litter decomposition. Moreover, litter mixing altered the abundance and diversity of bacterial and fungal communities on tomato root litter. Litter mixing also stimulated several putative keystone operational taxonomic units (OTUs) in the microbial correlation network, such as Fusarium sp. fOTU761 and Microbacterium sp. bOTU6632. Then, we isolated and cultured representative isolates of these two taxa, named Fusarium sp. F13 and Microbacterium sp. B26. Subsequent in vitro tests found that F13, but not B26, had strong decomposing ability; moreover, these two isolates developed synergistic interaction, thus promoted litter decomposition in coculture. Addition of F13 or B26 both promoted the decomposing activity of the resident decomposer community on tomato root litter, confirming their importance for litter decomposition. Overall, litter mixing promoted tomato root litter decomposition through altering microbial decomposers, especially through stimulating certain putative keystone taxa. IMPORTANCE Microbial decomposer community plays a key role in litter decomposition, which is an important regulator of soil carbon and nutrient cycling. Though changes in decomposer communities has been proposed as one of the potential underlying mechanisms driving the litter-mixture effects, direct evidence is still lacking. Here, we demonstrated that litter mixing stimulated litter decomposition through altering microbial decomposers at the early stage of decomposition. Moreover, certain putative keystone taxa stimulated by litter mixing contributed to the nonadditive litter-mixture effect. In vitro culturing validated the role of these taxa in litter decomposition. This study also highlights the possibility of regulating litter decomposition through manipulating certain microbial taxa.

Project description:Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the complexity of the communities. This challenge can be addressed, in part, by integrating the advances in DNA sequencing technology with computational approaches like machine learning. Although machine learning techniques have been applied to microbiome data, use of these techniques remains rare, and user-friendly platforms to implement such techniques are not widely available. We developed a tool that implements neural network and random forest models to perform regression and feature selection tasks on microbiome data. In this study, we applied the tool to analyze soil microbiome (16S rRNA gene profiles) and dissolved organic carbon (DOC) data from a 44-day plant litter decomposition experiment. The microbiome data includes 1709 total bacterial operational taxonomic units (OTU) from 300+ microcosms. Regression analysis of predicted and actual DOC for a held-out test set of 51 samples yield Pearson's correlation coefficients of.636 and.676 for neural network and random forest approaches, respectively. Important taxa identified by the machine learning techniques are compared to results from a standard tool (indicator species analysis) widely used by microbial ecologists. Of 1709 bacterial taxa, indicator species analysis identified 285 taxa as significant determinants of DOC concentration. Of the top 285 ranked features determined by machine learning methods, a subset of 86 taxa are common to all feature selection techniques. Using this subset of features, prediction results for random permutations of the data set are at least equally accurate compared to predictions determined using the entire feature set. Our results suggest that integration of multiple methods can aid identification of a robust subset of taxa within complex communities that may drive specific functional outcomes of interest.

Project description:What is the effect of phyllosphere microorganisms on litter decomposition in the absence of colonization by soil microorganisms? Here, we simulated the litter standing decomposition stage in the field to study the differences in the composition and structure of the phyllosphere microbial community after the mixed decomposition of Populus × canadensis and Pinus sylvestris var. mongolica litter. After 15 months of mixed decomposition, we discovered that litters that were not in contact with soil had an antagonistic effect (the actual decomposition rate was 18.18%, which is lower than the expected decomposition rate) and the difference between the litters themselves resulted in a negative response to litter decomposition. In addition, there was no significant difference in bacterial and fungal community diversity after litter decomposition. The litter bacterial community was negatively responsive to litter properties and positively responsive to the fungal community. Importantly, we found that bacterial communities had a greater impact on litter decomposition than fungi. This study has enriched our understanding of the decomposition of litter itself and provided a theoretical basis for further exploring the "additive and non-additive effects" of litter decomposition and the mechanism of microbial drive.ImportanceThe study of litter decomposition mechanism plays an important role in the material circulation of the global ecosystem. However, previous studies have often looked at contact with soil as the starting point for decomposition. But actually, standing litter is very common in forest ecosystems. Therefore, we used field simulation experiments to simulate the decomposition of litters without contact with soil for 15 months, to explore the combined and non-added benefits of the decomposition of mixed litters, and to study the influence of microbial community composition on the decomposition rate while comparing the differences of microbial communities.

Project description:Litter decomposition is a key process of nutrient and carbon cycling in terrestrial ecosystems. The decomposition process will likely be altered under ongoing climate change, both through direct effects on decomposer activity and through indirect effects caused by changes in litter quality. We studied how hydrological change indirectly affects decomposition via plant functional community restructuring caused by changes in plant species' relative abundances (community-weighted mean (CWM) traits and functional diversity). We further assessed how those indirect litter quality effects compare to direct effects. We set up a mesocosm experiment, in which sown grassland communities and natural turf pieces were subjected to different hydrological conditions (dryness and waterlogging) for two growing seasons. Species-level mean traits were obtained from trait databases and combined with species' relative abundances to assess functional community restructuring. We studied decomposition of mixed litter from these communities in a common "litterbed." These indirect effects were compared to effects of different hydrological conditions on soil respiration and on decomposition of standard litter (direct effects). Dryness reduced biomass production in sown communities and natural turf pieces, while waterlogging only reduced biomass in sown communities. Hydrological stress caused profound shifts in species' abundances and consequently in plant functional community composition. Hydrologically stressed communities had higher CMW leaf dry matter content, lower CMW leaf nitrogen content, and lower functional diversity. Lower CWM leaf N content and functional diversity were strongly related to slower decomposition. These indirect effects paralleled direct effects, but were larger and longer-lasting. Species mean traits from trait databases had therefore considerable predictive power for decomposition. Our results show that stressful soil moisture conditions, that are likely to occur more frequently in the future, quickly shift species' abundances. The resulting functional community restructuring will decelerate decomposition under hydrological stress.

Dataset Information

Microbial community composition controls carbon flux across litter types in early phase of litter decomposition.

Publications

Microbial community composition controls carbon flux across litter types in early phase of litter decomposition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets