PREreview of Short-term microbial community responses to an intense simulated precipitation pulse in the hyperarid Atacama Desert soil

This study used a microcosm experiment to simulate a 50 mm rainfall pulse on Atacama Desert soils and track soil chemistry and microbial communities over 7 days. Key results: The rain pulse caused rapid soil chemical shifts, δ¹³C enrichment, higher soil %C and SOM, and a drop in pH toward neutral (all significant, Fig. 2, p.5), while %N and δ¹⁵N remained unchanged. Microbial community composition changed significantly over time (PERMANOVA, p = 0.001, R²≈23% bacteria, 33% fungi; Table S1), although those shifts were not directly explained by measured soil chemistry (CCA, p > 0.3). Bacterial communities were dominated by Actinobacteria (~61%) initially, which declined, while Proteobacteria (~10%) increased post-pulse (Fig. 3a, p.6). Bacterial α-diversity stayed high and statistically unchanged (Shannon–Simpson indices stable, Fig. 3c). In contrast, fungal communities (82% Ascomycota) showed pronounced temporal turnover: e.g. Alternaria spiked to 54% relative abundance at 12 h, whereas Fusarium was dominant at 0 h (31%) and again by 168 h (33%) (Fig. 4b, p.7). Fungal richness rose in later stages (significant Chao1 increase, Fig. 4c). Predicted bacterial functions remained stable (mostly aerobic chemoheterotrophy; Fig. 5a), whereas fungi exhibited more varied functional potentials (e.g. plant pathogen and saprotroph roles tied to Alternaria mid-pulse vs. Fusarium at start/end; Fig. 5b). Contribution: This work demonstrates that even a brief extreme moisture event can rapidly reset soil microbial structure and functions in a hyperarid ecosystem. It highlights distinct survival strategies, bacteria maintain functional resilience, whereas fungi undergo larger compositional and functional shifts, suggesting fungal communities are less functionally redundant and potentially more fragile under pulse disturbances. Boundaries: The authors note that functional inferences are based on genomic potential (not direct activity). The experiment’s short duration and lab setting limit insight into long-term or field-scale responses, and rarefaction of sequencing data reduced fungal sample replicates at later time points, which may constrain interpretation of late-stage trends.

Major issues

• Rarefaction and Low Sequencing Depth (Moderate Validity Concern)  All samples were rarefied to extremely low read counts (145 reads for bacteria, 159 for fungi), causing loss of data and reducing replication (only 3 fungal replicates remained at 96 h and 168 h after rarefaction). This limited sequencing depth may undermine the validity of diversity and composition analyses, as true community differences could be masked by under-sampling, Methods (p.4, Section 2.4). Increase sequencing depth or use less restrictive normalization methods (rarefy to a higher read count or use relative abundance with statistical models) to retain more data. If additional sequencing isn’t possible, the authors should explicitly acknowledge in the Discussion that low sequencing depth and lost replicates limit confidence in the late-stage fungal dynamics and diversity comparisons.

• Lack of a No-Water Control (Moderate Design Validity) The study did not include a parallel control soil kept dry over the 7 days, so all microcosms received the simulated rain pulse. Without a no-pulse control, any changes observed over time (shifts in community or chemistry) cannot be unequivocally attributed to the water addition versus other factors (incubation conditions or natural temporal drift), Methods (Section 2.3, p.4). Acknowledge this design limitation and, if possible, provide or obtain control data (an unwatered soil incubated in the same chamber) to confirm that observed microbial changes are due to the moisture pulse rather than incubation artifacts. In future experiments, include a concurrent dry control treatment to strengthen causal inference.

• Inference of Function from Taxonomy (Moderate Interpretability Issue) Functional responses were deduced from predictive tools (FUNGuild for fungi, Tax4Fun2 for bacteria) rather than measured directly. The authors conclude, that fungal communities are less functionally stable and more fragile than bacteria, based on increases in predicted functional richness despite modest taxonomic change. However, these inferences rely on potential functions of detected taxa, not observed metabolic activity, Results 3.3 (p.7–8); Discussion (p.11–12). Temper the language about functional stability and fragility to make clear this is based on genomic potential. Ideally, support these claims with direct functional assays (respiration rates, enzyme activities, or metatranscriptomics) in future work. In the manuscript, explicitly note that functional changes are predicted and discuss this as a limitation when interpreting ecological implications.

Minor issues

• Typographical errors: Correct “PERMAVONA” to “PERMANOVA” in the Results (Section 3.1, p.5–6). Also, the reference to “Figure 2A” in the Discussion appears to be a mistake and should likely read “Figure 3a” (p.11).

• Nomenclature consistency: Use consistent taxonomic names (Actinobacteriota vs. Actinobacteria phylum) throughout the text to avoid confusion. Ehe phylum containing Rubrobacter is called Actinobacteriota in newer nomenclature, but the text uses “Actinobacteria” (Results 3.2), clarify that these refer to the same group.

• Clarity on “unique species”: In describing community composition changes, clarify that “unique species” refers to ASVs found exclusively at certain time points. The statement that early-stage bacterial communities had fewer unique species while fungi at 168 h showed the most unique composition could be misinterpreted, explicitly state this refers to unique ASVs per time point (Fig. 6, p.9) for precision.

• Report statistical support in text: When noting trends or differences, include the statistical significance or effect size in the text for transparency. Mention which bacterial phyla changed significantly over time (Actinobacteria and Proteobacteria; Fig. 3a) and report the p-values or ANOVA results supporting those changes, rather than relying solely on figure asterisks. Similarly, note the p-value for the increasing fungal Chao1 diversity (Fig. 4c) in the text.

• Data interpretation: Consider discussing absolute microbial abundance changes since DNA quantification (qPCR) was performed. The manuscript focuses on relative abundances; reporting whether total 16S or ITS gene copy numbers rose after the pulse would strengthen interpretation of microbial growth vs. community reshuffling. This addition (noting if total bacterial/fungal load increased post-precipitation) would give a fuller picture of the biomass response to hydration.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.