Effects of low wildfire burn severity due to pre-fire shrub thinning on the chaparral soil bacteriome in the Santa Monica Mountains of Southern California
Bibliographic record
Abstract
ABSTRACT Our objective was to study the longitudinal effect of decreased burn severity due to vegetation-type conversion (VTC) induced by chaparral shrub thinning prior to the Woolsey wildfire (November 2018) on soil chemistry and bacteriome composition and function. We compared soils from two study sites on the Malibu campus of Pepperdine University in the Santa Monica Mountains: one site had dense, unaltered chaparral shrubland and experienced a 4.5-fold increase in vegetation burn severity (high severity burn) compared to an adjacent altered site where the vegetative fuel load was 80% less prior to the fire (low severity burn). We analyzed soil nutrient concentrations and pH in 2019 and 2021 and soil respiration, measured by CO 2 efflux, in 2019, 2020, and 2021. DNA was isolated from soil samples collected in 2019, 2020, 2021, and 2023 for Illumina Miseq paired-end 16S V3-V4 sequencing. We predicted the functional bacteriome from the 16S data using PICRUSt2. Relative to high severity soils, low severity soils showed decreased nutrient concentrations, pH, and % organic matter in 2019. The low severity burned site showed greater compositional stability over time, with increased pyrophilous taxa in 2021 and 2023 ( Massilia , Conexibacter , etc.). High severity burned soils showed decreased metabolic capacity over time. We identified correlations between bacterial taxa and diversity and functional pathways, which remained only in the high severity soil samples after stratification. Our findings contribute to an improved understanding of bacterial succession in soil from sites that experienced VTC prior to wildfire, highlighting microbial ecological implications for fire management strategies. IMPORTANCE Along with increased fire frequency, the wildfire-urban interface has been expanding, requiring the need for fire mitigation strategies, such as pre-fire vegetation thinning near urban structures. Pre-fire vegetation thinning contributes to vegetation-type conversion and decreases burn severity, but its effect on the soil microenvironment is largely unknown. Here, we compared soil sites that experienced burns of different severity due to pre-fire vegetation thinning and vegetation-type conversion at one site but not the other. We identified changes in soil chemistry and longitudinal shifts in soil bacterial abundance and metabolic capacity that are associated with decreased burn severity due to pre-fire vegetation-type conversion. Our work contributes to improved understanding of the effects of pre-fire vegetation thinning to manage wildfire impact on urban structures on the soil microenvironment. These findings demonstrate ecological implications for fire management strategies and recovery of the chaparral ecosystems following wildfire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".