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Soils contain more carbon than the atmosphere and vegetation combined. An increased flow of carbon from the atmosphere into soil pools could help mitigate anthropogenic emissions of carbon dioxide and climate change. Yet we do not know how quickly soils might respond because the age distribution of soil carbon is uncertain. Here we used 789 radiocarbon (∆14C) profiles, along with other geospatial information, to create globally gridded datasets of mineral soil ∆14C and mean age. We found that soil depth is a primary driver of ∆14C, whereas climate (for example, mean annual temperature) is a major control on the spatial pattern of ∆14C in surface soil. Integrated to a depth of 1 m, global soil carbon has a mean age of 4,830 ± 1,730 yr, with older carbon in deeper layers and permafrost regions. In contrast, vertically resolved land models simulate ∆14C values that imply younger carbon ages and a more rapid carbon turnover. Our data-derived estimates of older mean soil carbon age suggest that soils will accumulate less carbon than predicted by current Earth system models over the twenty-first century. Reconciling these models with the global distribution of soil radiocarbon will require a better representation of the mechanisms that control carbon persistence in soils.Soils may accumulate less carbon and with a slower turnover than Earth system models predict, according to analysis of the age distribution of global soil carbon, which finds that the mean age of soil carbon is older than that in simulated in models.

Bacteria and fungi drive decomposition, a fundamental process in the carbon cycle, yet the importance of microbial community composition for decomposition remains elusive. Here, we used an 18-month reciprocal transplant experiment along a climate gradient in Southern California to disentangle the effects of the microbial community versus the environment on decomposition. Specifically, we tested whether the decomposition response to climate change depends on the microbial community. We inoculated microbial decomposers from each site onto a common, irradiated leaf litter within “microbial cages” that prevent microbial exchange with the environment. We characterized fungal and bacterial composition and abundance over time and investigated the functional consequences through litter mass loss and chemistry. After 12 months, microbial communities altered both decomposition rate and litter chemistry. Further, the functional measurements depended on an interaction between the community and its climate in a manner not predicted by current theory. Moreover, microbial ecologists have traditionally considered fungi to be the primary agents of decomposition and for bacteria to play a minor role. Our results indicate that not only does climate change and transplantation have differential legacy effects among bacteria and fungi, but also that bacterial communities might be less functionally redundant than fungi with regards to decomposition. Thus, it may be time to reevaluate both the role of microbial community composition in its decomposition response to climate and the relative roles of bacterial and fungal communities in decomposition.

In most ecosystems, the degradation of complex organic material depends on extracellular enzymes produced by microbes. These enzymes can exist in bound or free form within the soil, but the dynamics of these different enzyme pools remain uncertain. To address this uncertainty, I determined rates of enzyme turnover in a volcanic soil with and without added enzymes. I also tested whether or not soil minerals and humic acids would alter enzyme activity. In soils that were gamma-irradiated to stop enzyme production, 35-70% of the enzyme activity was stable throughout the 21-day incubation. The remaining enzyme fraction decayed at rates ranging from - 0.032 to - 0.628 day-¹. In both the irradiated soils and in soils with added enzymes, addition of the mineral allophane had a strong positive effect on most enzyme activities. Another added mineral, ferrihydrite, had a weak positive effect on some enzymes. Added humic acids strongly inhibited enzyme activity. These findings suggest that minerals, especially allophane, enhance potential enzyme activities in young volcanic soils. However, the actual activity and function of these enzymes may be low under field conditions if stabilization results in less efficient enzyme-substrate interactions. If this is the case, then much of the measured enzyme activity in bulk soil may be stabilized but unlikely to contribute greatly to ecosystem processes.

Rates of litter decomposition in dryland ecosystems are consistently underestimated by decomposition models driven by temperature, moisture, and litter chemistry. The most common explanation for this pattern is that ultraviolet radiation (UV) increases decomposition through photodegradation of the litter lignin fraction. Alternatively, UV could increase decomposition through effects on microbial activity. To assess the mechanisms underlying UV photodegradation in a semiarid climate, we exposed high- and low-lignin litter to ambient and blocked UV over 15 months in a Mediterranean ecosystem. We hypothesized that UV would increase litter mass loss, that UV would preferentially increase mass loss of the lignin fraction, and that UV would have a negative effect on microbial activity. Consistent with our first hypothesis, we found that UV-blocking reduced litter mass loss from 16% to 1% in high-lignin litter and from 29% to 17% in low-lignin litter. Contrary to our second hypothesis, UV treatment did not have a significant effect on lignin content in either litter type. Instead, UV-blocking significantly reduced cellulose and hemicellulose mass loss in both litter types. Contrary to our third hypothesis, we observed a positive effect of UV on both fungal abundance and the potential activities of several assayed extracellular enzymes. Additionally, under ambient UV only, we found significant correlations between potential activities of cellulase and oxidase enzymes and both the concentrations and degradation rates of their target compounds. Our results indicate that UV is a significant driver of litter mass loss in Mediterranean ecosystems, but not solely because UV directly degrades carbon compounds such as lignin. Rather, UV facilitates microbial degradation of litter compounds, such as cellulose and hemicellulose. Thus, unexpectedly high rates of litter decomposition previously attributed directly to UV in dryland ecosystems may actually derive from a synergistic interaction between UV and microbes.

Although it is generally accepted that plant community composition is key for predicting rates of ecosystem processes in the face of global change, microbial community composition is often ignored in ecosystem modeling. To address this issue, we review recent experiments and assess whether microbial community composition is resistant, resilient, or functionally redundant in response to four different disturbances. We find that the composition of most microbial groups is sensitive and not immediately resilient to disturbance, regardless of taxonomic breadth of the group or the type of disturbance. Other studies demonstrate that changes in composition are often associated with changes in ecosystem process rates. Thus, changes in microbial communities due to disturbance may directly affect ecosystem processes. Based on these relationships, we propose a simple framework to incorporate microbial community composition into ecosystem process models. We conclude that this effort would benefit from more empirical data on the links among microbial phylogeny, physiological traits, and disturbance responses. These relationships will determine how readily microbial community composition can be used to predict the responses of ecosystem processes to global change.

Rates of ecosystem processes such as decomposition are likely to change as a result of human impacts on the environment. In southern California, climate change and nitrogen (N) deposition in particular may alter biological communities and ecosystem processes. These drivers may affect decomposition directly, through changes in abiotic conditions, and indirectly through changes in plant and decomposer communities. To assess indirect effects on litter decomposition, we reciprocally transplanted microbial communities and plant litter among control and treatment plots (either drought or N addition) in a grassland ecosystem. We hypothesized that drought would reduce decomposition rates through moisture limitation of decomposers and reductions in plant litter quality before and during decomposition. In contrast, we predicted that N deposition would stimulate decomposition by relieving N limitation of decomposers and improving plant litter quality. We also hypothesized that adaptive mechanisms would allow microbes to decompose litter more effectively in their native plot and litter environments. Consistent with our first hypothesis, we found that drought treatment reduced litter mass loss from 20.9% to 15.3% after six months. There was a similar decline in mass loss of litter inoculated with microbes transplanted from the drought treatment, suggesting a legacy effect of drought driven by declines in microbial abundance and possible changes in microbial community composition. Bacterial cell densities were up to 86% lower in drought plots and at least 50% lower on litter derived from the drought treatment, whereas fungal hyphal lengths increased by 13-14% in the drought treatment. Nitrogen effects on decomposition rates and microbial abundances were weaker than drought effects, although N addition significantly altered initial plant litter chemistry and litter chemistry during decomposition. However, we did find support for microbial adaptation to N addition with N-derived microbes facilitating greater mass loss in N plots than in control plots. Our results show that environmental changes can affect rates of ecosystem processes directly through abiotic changes and indirectly through microbial abundances and communities. Therefore models of ecosystem response to global change may need to represent microbial biomass and community composition to make accurate predictions.

The factors driving β-diversity (variation in community composition) yield insights into the maintenance of biodiversity on the planet. Here we tested whether the mechanisms that underlie bacterial β-diversity vary over centimeters to continental spatial scales by comparing the composition of ammonia-oxidizing bacteria communities in salt marsh sediments. As observed in studies of macroorganisms, the drivers of salt marsh bacterial β-diversity depend on spatial scale. In contrast to macroorganism studies, however, we found no evidence of evolutionary diversification of ammonia-oxidizing bacteria taxa at the continental scale, despite an overall relationship between geographic distance and community similarity. Our data are consistent with the idea that dispersal limitation at local scales can contribute to β-diversity, even though the 16S rRNA genes of the relatively common taxa are globally distributed. These results highlight the importance of considering multiple spatial scales for understanding microbial biogeography.

Understanding the relationship between prokaryotic traits and phylogeny is important for predicting and modeling ecological processes. Microbial extracellular enzymes have a pivotal role in nutrient cycling and the decomposition of organic matter, yet little is known about the phylogenetic distribution of genes encoding these enzymes. In this study, we analyzed 3058 annotated prokaryotic genomes to determine which taxa have the genetic potential to produce alkaline phosphatase, chitinase and β-N-acetyl-glucosaminidase enzymes. We then evaluated the relationship between the genetic potential for enzyme production and 16S rRNA phylogeny using the consenTRAIT algorithm, which calculated the phylogenetic depth and corresponding 16S rRNA sequence identity of clades of potential enzyme producers. Nearly half (49.2%) of the genomes analyzed were found to be capable of extracellular enzyme production, and these were non-randomly distributed across most prokaryotic phyla. On average, clades of potential enzyme-producing organisms had a maximum phylogenetic depth of 0.008004–0.009780, though individual clades varied broadly in both size and depth. These values correspond to a minimum 16S rRNA sequence identity of 98.04–98.40%. The distribution pattern we found is an indication of microdiversity, the occurrence of ecologically or physiologically distinct populations within phylogenetically related groups. Additionally, we found positive correlations among the genes encoding different extracellular enzymes. Our results suggest that the capacity to produce extracellular enzymes varies at relatively fine-scale phylogenetic resolution. This variation is consistent with other traits that require a small number of genes and provides insight into the relationship between taxonomy and traits that may be useful for predicting ecological function.

Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.

Microbial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling. Microbial carbon use efficiency (CUE) is crucial for carbon storage, but its variability is difficult to capture due to inconsistent measurements and complex interactions. This perspective proposes integrating diverse data and models to improve CUE in carbon cycle models