Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment
- State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, China
- Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, China
- Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, China
Figures
Figure 1
Field experiment design for simulated warming and altered precipitation, qSIP incubation, and the growth responses of soil bacteria to changing climate regimes.
To examine the effects of warming and altered precipitation on an alpine grassland ecosystem, two levels of temperature (T0, T+), and three levels of precipitation (-P, nP, +P) were established in 2011. The soil samples were collected in 2020 and used for 18O-qSIP incubation (A). Potential interaction types between multiple climate factors (B). The diagram shows that two factors (X and Y) of warming and altered precipitation impact a biological response in the same direction (upper) or have opposing effects on when acting separately. Their combined effect could be additive, that is the sum of the two factor effects. Alternatively, the interaction types can be antagonistic or synergistic. Null model (we use the additive expectation as the null model here) provides the threshold for distinguishing between these interactions.
Figure 2
Species-specific shifts of 18O excess atom fraction (EAF-18O).
Bars represent 95% confidence intervals (CIs) of OTUs. Each circle represents an OTU and color indicates phylum. The open circles with gray bars represent OTUs with 95% CI intersected with zero (indicating no significant 18O enrichment); Closed circles represent the OTUs enriched 18O significantly, whose 95% CIs were away from zero (i.e. the OTUs had detectable growth).
Figure 3 with 1 supplement
Bacterial growth responses to climate change and the interaction types between warming and altered precipitation.
The growth rates (A), and responses of soil bacteria to warming and altered precipitation (B) at the whole community level. The growth rates (C), and responses of the dominant bacterial phyla (D) had similar trends with that of the whole community. Error bars depict means ± SD (n = 3). Different letters indicate significant differences between climate treatments (p < 0.05). The p-values were calculated using a two-tailed Student’s t-test. Two-way ANOVA was used to examine the effects of climate factors on bacterial growth (**: p ≤ 0.01, ***: p ≤ 0.001, ns: no significance). ‘W×P’: the interaction effects of warming and altered precipitation; ‘W×D’: warming and drought scenario; ‘W×W’: warming and wet scenario.
Figure 3—figure supplement 1
The growth responses of grassland bacteria to warming and altered precipitation based on ZOTU (zero-radius OTU) analysis.
The results of growth rates at the community level (A), the phylum level (B), and the ZOTU level (C and D) were similar to those based on OTU analysis. (C) The single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. (D) The proportions of species growth influenced by different interaction types of T and P. Error bars depict means ± SD (n = 3). Different letters indicate significant differences between treatments (p < 0.05). The p-values were calculated using a two-tailed Student’s t-test.T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0 +P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. The sequences were quality-filtered using the USEARCH v.11.0. The paired-end sequences were merged and then quality filtered with ‘fastq_mergepairs’ and ‘fastq_filter’ commands, respectively. Sequences < 370 bp and total expected errors > 0.5 were removed. Next, ‘fastx_uniques’ command was implemented to identify the unique sequences. Denoising attempts to identify all correct biological sequences in the reads, which is done by the ‘unoise3’ command. A denoised sequence is called a ‘ZOTU’ (zero-radius OTU).
Figure 4 with 2 supplements
The growth responses and phylogenetic relationship of incorporators subjected to different interaction types under two climate scenarios.
A phylogenetic tree of all incorporators observed in the grassland soils (A). The inner heatmap represents the single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. The outer heatmap represents the interaction types between warming and altered precipitation under two climate change scenarios. The proportions of positive or negative responses in species growth to single and combined manipulation of climate factors by summarizing the data from the inner heatmap (B). The proportions of species growth influenced by different interaction types under two climate change scenarios by summarizing the data from the outer heatmap (C).
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Figure 4—source data 1
The nearest taxon index (NTI) for incorporators subjected to different interaction types under two climate change scenarios.
- https://cdn.elifesciences.org/articles/89392/elife-89392-fig4-data1-v1.xlsx
Figure 4—figure supplement 1
The growth responses of grassland bacteria at the genus level to warming and altered precipitation based on OTU analysis (A and C) and ZOTU analysis (B and D).
The single and combined factor effects of climate factors on growth in genera, by comparing with those in T0nP (A and B). The proportions of genera whose growth influenced by different interaction types of T and P (C and D). T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0 +P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation.
Figure 4—figure supplement 2
The higher level of antagonism of wet × warming than that of drought × warming.
The antagonistic intensities were assigned to the five interaction types on a 5-point scale, from –1 to 3 for synergistic, additive, weak antagonistic, strong antagonistic and nullifying effect, respectively (A). The overall antagonistic intensities of all incorporators under warming × drought and warming × wet scenarios were estimated by weighting the relative proportions of incorporators subjected to different interaction types (B). T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0 +P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation.
Figure 5
Within-species shift in interaction types contributed to the variance of the whole community growth response under two climate scenarios.
Venn plots showing the overlaps of incorporators, and their interaction types between two climate scenarios (A). The phylogenetic relationship of the 215 incorporators whose growth dynamics were influenced by the weak antagonistic interaction of warming × drought and by the neutralizing effect of warming × wet (B). The blue-green bars represent the average growth rates of incorporators across different climate treatments. The heatmap displayed the potential functions associated with carbon and nutrient cycles predicted by Picrust2. The values of function potential were standardized (range: 0–1). ‘W×D’ represents warming × drought and ‘W×W’ represents warming × wet.
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Figure 5—source data 1
Species and genomic information of the dominant active taxa in grassland soil under climate change conditions.
- https://cdn.elifesciences.org/articles/89392/elife-89392-fig5-data1-v1.xlsx
Author response image 1
The growth responses of grassland bacteria to warming and altered precipitation based on ZOTU analysis.
The results of growth rates at the community level (A), the phylum level (B), and the ZOTU level (C and D) were similar to those based on OTU analysis. (C) The single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. (D) The proportions of species growth influenced by different interaction types of T and P. T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0+P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. Values represent mean and the error bars represent standard deviation. Different letters indicate significant differences between climate treatments.
Author response image 2
The growth responses of grassland bacteria at the genus level to warming and altered precipitation based on OTU analysis (A and C) and ZOTU analysis (B and D).
(A and B) The single and combined factor effects of climate factors on growth in genera, by comparing with those in T0nP. (C and D) The proportions of genera whose growth influenced by different interaction types of T and P.
Author response image 3
The distribution of 16S rRNA gene abundance of three representative bacterial taxa (OTU1- Bradyrhizobium, OTU14-Solirubrobacter, and OTU15-Pseudoxanthomonas) in different buoyant density fractions.
Values represent mean and the error bars represent standard deviation.
Author response image 4
Relative abundance of 16S rRNA gene copies in each fraction.
The fractions with density between 1.703 and 1.727 g ml-1 were selected because they contained more than 99% gene copy numbers of each sample. T0-P represents the ambient temperature and decreased precipitation; T+-P represents warming and decreased precipitation; T0cP represents ambient temperature and precipitation; T+cP represents warming and ambient precipitation; T0+P represents ambient temperature and enhanced precipitation; T++P represents warming and enhanced precipitation. Values represent mean and the error bars represent standard deviation.
Author response image 5
Bacterial growth responses to climate change and the interaction types between warming and altered precipitation.
The growth rates (A), and responses (LnRR) of soil bacteria to warming and altered precipitation (B) at the whole community level. The growth rates (C), and responses of the dominant bacterial phyla (D) had similar trends with that of the whole community. Values represent mean and the error bars represent standard deviation. Different letters indicate significant differences between climate treatments.
Author response image 6
The growth responses and phylogenetic relationship of incorporators subjected to different interaction types under two climate scenarios.
A phylogenetic tree of all incorporators observed in the grassland soils (A). The inner heatmap represents the single and combined factor effects of climate factors on species growth, by comparing with the growth rates in T0nP. The outer heatmap represents the interaction types between warming and altered precipitation under two climate change scenarios. The proportions of positive or negative responses in species growth to single and combined manipulation of climate factors by summarizing the data from the inner heatmap (B). The proportions of species growth influenced by different interaction types of T and P by summarizing the data from the outer heatmap (C).
Tables
Author response table 1
Soil DNA concentration in climate change treatments after qSIP incubation (measured by Qubit DNA HS Assay Kits).
| ng//mul | T^(0)-P | T^(+)-P | T^(0)cP | T^(+)cP | T^(0)+P | T^(+)+P | F value | p value |
|---|---|---|---|---|---|---|---|---|
| ^(16) O-treatment | 98+-11 | 100+-11 | 98+-19 | 109+-11 | 95+-17 | 93+-20 | 0.34 | 0.88 |
| ^(18) O-treatment | 105+-21 | 99+-13 | 83+-24 | 98+-5 | 75+-13 | 78+-18 | 1.49 | 0.27 |
| F value | 0.21 | 0.02 | 0.71 | 2.17 | 2.47 | 0.92 | ||
| p value | 0.7 | 0.9 | 0.45 | 0.22 | 0.19 | 0.39 |
Additional files
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MDAR checklist
- https://cdn.elifesciences.org/articles/89392/elife-89392-mdarchecklist1-v1.pdf
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Source code 1
The qSIP calculation procedures and R code for calculating species growth rates.
- https://cdn.elifesciences.org/articles/89392/elife-89392-code1-v1.zip
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Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment
eLife 12:RP89392.
https://doi.org/10.7554/eLife.89392.3
