New Biomass Estimates for Chaparral-Dominated Southern California Landscapes

Food for all? Wildfire ash fuels growth of diverse eukaryotic plankton

In December 2017, one of the largest wildfires in California history, the Thomas Fire, created a large smoke and ash plume that extended over the northeastern Pacific Ocean. Here, we explore the impact of Thomas Fire ash deposition on seawater chemistry and the growth and composition of natural microbial communities. Experiments conducted in coastal California waters during the Thomas Fire revealed that leaching of ash in seawater resulted in significant additions of dissolved nutrients including inorganic nitrogen (nitrate, nitrite and ammonium), silicic acid, metals (iron, nickel, cobalt and copper), organic nitrogen and organic carbon. After exposure to ash leachate at high (0.25 g ash l-1) and low (0.08 g ash l-1) concentrations for 4 days, natural microbial communities had 59-154% higher particulate organic carbon concentrations than communities without ash leachate additions. Additionally, a diverse assemblage of eukaryotic microbes (protists) responded to the ash leachate with taxa from 11 different taxonomic divisions increasing in relative abundance compared with control treatments. Our results suggest that large fire events can be important atmospheric sources of nutrients (particularly nitrogen) to coastal marine systems, where, through leaching of various nutrients, ash may act as a 'food for all' in protist communities.

Biomass Assessment and Carbon Sequestration in Post-Fire Shrublands by Means of Sentinel-2 and Gaussian Processes

In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, Spain) in 2012. Sentinel-2 imagery was used together with our own forest inventories in 2020 and machine learning methods to assess the total biomass of the area. The inventory includes plots of total dry weight ranging between 6 and 14 Mg·ha−1 with individuals up to 8 years old. Nonlinear, nonparametric Gaussian process regression methods were applied to link reflectance values from Sentinel-2 imagery with total shrub biomass. With a reduced inventory of only 32 plots covering 136 ha, the total biomass could be assessed with a root-mean-square error of 1.36 Mg·ha−1 and a bias of −0.04 Mg·ha−1, getting a relative error between 9.8% and 20.4% for the gathered biomass. This is a rather good estimation considering the little effort and time invested; thus, the suggested methodology is very suitable for forest monitoring and management.