Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment

Abstract

Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil. However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation. We used the ¹⁸O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation. Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40-90% of growth reduction of whole community. The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet. The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes. These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors.

Keywords: altered precipitation; ecology; microbial population growth; multifactor interaction; quantitative stable isotope probing; the Tibetan Plateau; warming.

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.. 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.. 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).

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.

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).