Explaining soil biogeochemical responses after fire

Abstract

Wildfires are a perennial event globally and the biogeochemical underpinnings of soil responses at relevant spatial and temporal scales are unclear. Soil biogeochemical processes regulate plant growth and nutrient losses that affect water quality, yet the response of soil after variable intensity fire is difficult to explain and predict. To address this issue, we investigated fire-impacted soil through two independent lenses.

First, we examined two wildfires in Colorado, USA across the first and second post-fire years and leveraged Statistical Learning (SL) to predict and explain biogeochemical responses. We found that SL predicts biogeochemical responses in soil after wildfire with surprising accuracy. Of the 13 biogeochemical analytes analyzed in this study, 9 are best explained with a hybrid microbiome + biogeochemical SL model. Biogeochemical-only models best explain 3 features, and 1 feature is explained equally well with hybrid or biogeochemical-only models. In some cases, microbiome-only SL models are also effective (such as predicting NH4+). Whenever a microbiome component is employed, selected features always involve uncommon soil microbiota (i.e., the 'rare biosphere', existing at < 1% relative abundance).

Second, an excavated soil cube was burned in the laboratory with a butane torch (~ 1300 °C). Using optical chemical sensing with planar ‘optodes’, pH and dissolved O2 concentration were tracked spatially (depth and width) with a resolution of 360 µm per pixel for 72 hours. We show that imaging data from planar optodes can be correlated with microbial activity as quantified via high-throughput sequencing of RNA transcripts. Of the 28 carbon, nitrogen, and sulfur metabolism pathways (sets of genes) or individual genes investigated in this study, only two correlated significantly with soil depth postfire (which covaries with postfire soil temperature). In contrast, postfire pH negatively correlated with four of the 28 pathways or genes, and dissolved O2 negatively correlated with 10 of 28. Results demonstrate that postfire soils are spatially complex on a mm scale and that using optode-based chemical imaging as a chemical navigator for transcript sampling is effective.