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dcyphr | Microbial diversity drives carbon use efficiency in a model soil

Abstract

Understanding of the effect of warming, drought, and diversity loss on carbon in soil is limited. Microbial carbon use efficiency (CUE) is important in the carbon cycle, but how biological and non-biological factors influence it is unclear. In this study, the researchers combine microbes (biological) with different temperature and moisture levels (non-biological) to understand microbial diversity and community composition in soil. The researchers found that while microbial diversity and community composition are the strongest predictors of CUE, CUE was only positively associated with microbial diversity in high-moisture soil. These results indicate that the relationship between microbial diversity and the functioning of ecosystems can be inhibited in unfavorable soil conditions. However, people should still consider global climate change’s impact on soil to understand changes in the carbon cycle.

Aims

The researchers wanted to understand how biological factors (microbial diversity and community composition) and non-biological factors (temperature and moisture) impacted the soil phase in the carbon cycle.

Introduction

The way ecosystems function are threatened by climate change and losses in biodiversity. One function under threat is the soil phase of the carbon cycle. Carbon storage in soil is crucial for regulating the climate, and is partially regulated by microbes in the soil. Predicting carbon storage in soil is dependent on microbial carbon use efficiency (CUE), or the portion of carbon taken up by microbes and retained rather than being used for respiration. Global climate change has been shown to impact CUE as well as microbial diversity and community structure by impacting soil temperature and moisture. Generally, high temperatures increase respiration but not growth, decreasing CUE. Although the impact of soil moisture on CUE is not well-studied, low-moisture conditions are thought to increase CUE due to increased energy expenditure on maintaining hydration inside microbial cells.  In addition, low water availability can decrease the supply of substrates in the soil, such that more substrates go toward metabolism instead of growth. Temperature and moisture can also drive changes in microbial diversity and community structure, impacting CUE.


The impact of microbial diversity and community structure on CUE has not yet been explored. Previous studies have shown positive associations between microbial diversity and soil functions--however, large-scale soil processes like carbon cycling are thought to be more resistant to changes caused by diversity. Nonetheless, some studies have shown that even broad processes like carbon cycling can show a positive relationship with microbial diversity and community composition. In addition, it has been shown that non-biological factors can impact the relationship between diversity and growth.

This study sought to understand the influence of temperature, moisture, and microbial diversity and community composition on CUE. The researchers hypothesized that (1) more microbial diversity leads to higher CUE, (2) increase in temperature and decrease in soil moisture decrease CUE, and (3) nonbiological factors influence the relationship between microbial diversity and CUE. The researchers developed a model soil to capture all variations in soil across space and time. They extracted microbial communities from natural soil and manipulated their diversity. The manipulated communities were grown in the model soil under two different temperatures (15 and 25ºC) and soil moistures (30% and 60%

%) for a total of 200 samples. After 120 days, the researchers measured CUE and assessed microbial diversity. The researchers found that microbial diversity is positively associated with CUE under high-moisture conditions. They also found that temperature and moisture indirectly influence CUE by altering microbial community composition.

Results and Discussion

Microbial community assembly in model soils

The researchers found that 1036 kinds of bacteria and 270 kinds of fungi grew in the model soils. Manipulations as described in the methods section successfully altered microbial diversity in model soils. Bacterial and fungal diversity were evaluated after 120 days of growth in model soil using qPCR. Overall, the researchers were able to create communities with great diversity and community structure in the model soil. Diversity was driven by the researchers’ manipulation, while community composition was influenced by temperature and moisture conditions. Although the communities in model soil were less diverse than in nature, they were more diverse than previous studies manipulating microbial diversity. The experimental design for manipulating microbial diversity can be understood in Figure 1.


Empirical link between diversity and CUE

The researchers found higher CUE in more diverse communities compared with less diverse communities. To understand how CUE is affected by diversity, the researchers evaluated growth and respiration responses. They found no significant relationship between fungal diversity and CUE, but found that bacterial diversity was more positively correlated with growth rate under high-moisture conditions (Figure 2a) than respiration (Figure 2b). Thus, there was a positive relationship between bacterial diversity and CUE in high-moisture soils (Figure 2c). However, previous studies have found this relationship to be positive, neutral, or negative. This may be a result of differences in diversity within different experiments.


One explanation of this positive association between bacterial diversity and CUE is complementarity effects, which increase overall community productivity due to inter-species interactions between organisms. Complementarity effects have been shown to vary based on abiotic conditions. The researchers propose that high-moisture soil allows greater interaction between species, such as “cross-feeding;” for example, an amino acid one species produces becomes a substrate for another, thus resulting in more efficient and less energetically expensive growth (higher CUE). On the other hand, low moisture could have limited the complementary interactions between species, explaining the absence of a positive relationship between diversity and CUE.


Temperature and moisture effects on CUE

The researchers found that temperature and moisture both drive CUE, agreeing with previous studies. Lower CUE was observed in samples incubated at higher temperatures, which was associated with an increase in estimated rrN copy number (associated with lower growth efficiency). Moisture did not significantly impact CUE or rrN. Because microbial communities differed based on long-term temperature and moisture conditions, the researchers also evaluated the effect of short-term temperature and moisture changes.


To do this, the researchers measured CUE for all abiotic condition combinations for samples with similar diversity levels. The researchers found that a short-term increase of 10ºC significantly increased both respiration and growth, which did not result in a significant change in CUE. On the other hand, short-term changes in moisture had a stronger impact; an increase from 30% to 60% moisture increased respiration and growth by 146% and 169% respectively, resulting in an 8% increase in CUE. The researchers hypothesize that higher growth was possibly due to higher nutrient and substrate availability after increasing water content.


CUE as a function of interactions between biotic and abiotic drivers

The researchers used structural equation modeling (SEM) to determine how biological factors impact the influence of nonbiological factors on CUE. Modeling was based on the assumption that nonbiological factors drive CUE directly, but also indirectly through influencing the biological factors. The researchers used the model to test 5 hypotheses: (1) different community compositions will result in different CUEs, (2) bacterial diversity increases CUE, (3) extracellular enzymes represent a cost to microbial growth, decreasing CUE, (4) the presence of fungi increases CUE, and (5) soil aggregation resulting from microbes have a positive effect on CUE. Overall, the model explained 30% of variances in CUEs in different communities. The model can be seen in Figure 4.


The model indicated that temperature and moisture only influenced CUE indirectly, acting through influencing biological factors. Overall, bacterial community composition and diversity were the strongest direct influencers of CUE, and others included the presence of fungi, extracellular enzymatic activity, and soil aggregation. 

Nonetheless, many factors that influence CUE remain unexplained in the model and need further research.

Methods

Model soil, inoculation, and incubation conditions

The researchers created a sterile, model soil free of carbon to study biological and nonbiological factors that influence CUE. They collected microbial communities from soil in a temperate deciduous forest. They then manipulated their samples to create different levels of diversity, including dilution, using a filter to exclude fungi, and heating to induce sporulation. After manipulation, communities were introduced to different “microcosms.” These microcosms were incubated at two different water treatments (30% or 60% moisture) and two different temperatures (15ºC or 25ºC).

After 120 days of incubation, microcosms were harvested to collect data.


Quantitative real-time PCR (qPCR)

qPCR was used to assess the abundance of bacteria and fungi in each microcosm.


Potential extracellular enzymatic activity

Extracellular enzyme potentials were assayed to determine nitrogen and carbon-cycling enzymes.


Soil aggregation

Soil aggregation was determined with a modified, water-stable protocol.


Total C and N

Soil from microcosms was dried and analyzed for total carbon and nitrogen using elemental analysis.


Bacterial and fungal diversity

Illumina sequencing was performed on extracted DNA from each microcosm to identify bacterial and fungal species and determine the diversity of each microcosm.


Statistical analysis

All statistical analyses were performed in R.

Conclusion

To confront climate change, it is imperative to understand how global environmental changes impact the carbon cycle. This is the first study that manipulated microbial communities in soil to understand interactions between biological and nonbiological factors and CUE. The results of this study show that shifts in microbial communities impact CUE.