Rayo Pinto

Introduction: The response of soil microbial communities to fire-induced changes in nutrients is a key determinant of post-fire ecosystem dynamics. In Pinus sylvestris forests, we investigated how bacterial and fungal communities are shaped by nutrient availability and soil stoichiometric relationships 2 years after wildfire across a gradient of fire severity. Methods: We analyzed soils from unburned, low-severity, and high-severity fire conditions. Soil carbon fractions, including total, organic, inorganic, and pyrogenic carbon, nitrogen, available phosphorus, and stoichiometric ratios (C:N, C:P, and N:P) were quantified. Bacterial and fungal communities were characterized using 16S rRNA gene and ITS amplicon sequencing, respectively. Results: Available phosphorus and nutrient stoichiometric ratios were key drivers of microbial community composition 2 years after fire. Fungal taxa associated with nutrient concentrations or ratios showed balanced responses, with similar proportions of fungal communities being positively and negatively affected. In contrast, several prokaryotic taxa showed positive associations with post-fire nutrient conditions and appeared to benefit from them, including Microbacteriaceae, Geodermatophilaceae, and Sphingomonadaceae. Discussion: The identification of specific microbial families linked to nutrient availability highlights their potential as indicators of soil recovery. These findings demonstrate the importance of soil stoichiometric relationships in shaping microbial communities in post-fire ecosystems and contribute to a better understanding of soil recovery processes by linking nutrient dynamics with microbial changes after fire.

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R²cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R²cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.