Effects of litter and root manipulations on soil carbon and nitrogen in a Schrenk's spruce (Picea schrenkiana) forest

Effects of Litter and Root Manipulations on Soil Bacterial and Fungal Community Structure and Function in a Schrenk's Spruce (Picea schrenkiana) Forest

Soil microorganisms are the key driver of the geochemical cycle in forest ecosystem. Changes in litter and roots can affect soil microbial activities and nutrient cycling; however, the impact of this change on soil microbial community composition and function remain unclear. Here, we explored the effects of litter and root manipulations [control (CK), doubled litter input (DL), litter removal (NL), root exclusion (NR), and a combination of litter removal and root exclusion (NI)] on soil bacterial and fungal communities and functional groups during a 2-year field experiment, using illumina HiSeq sequencing coupled with the function prediction platform of PICRUSt and FUNGuild. Our results showed that litter and root removal decreased the diversity of soil bacteria and fungi (AEC, Shannon, and Chao1). The bacterial communities under different treatments were dominated by the phyla Proteobacteria, Acidobacteria, and Actinomycetes, and NL and NR reduced the relative abundance of the first two phyla. For the fungal communities, Basidiomycetes, Ascomycota, and Mortierellomycota were the dominant phyla. DL increased the relative abundance of Basidiomycetes, while NL and NR decreased the relative abundance of Ascomycota. We also found that litter and root manipulations altered the functional groups related to the metabolism of cofactors and vitamins, lipid metabolism, biosynthesis of other secondary metabolites, environmental adaptation, cell growth, and death. The functional groups including ectomycorrhizal, ectomycorrhizal-orchid mycorrhizal root-associated biotrophs and soil saprotrophs in the fungal community were also different among the different treatments. Soil organic carbon (SOC), pH, and soil water content are important factors driving changes in bacterial and fungal communities, respectively. Our results demonstrate that the changes in plant detritus altered the soil microbial community structure and function by affecting soil physicochemical factors, which provides important data for understanding the material cycle of forest ecosystems under global change.

Large-scale forecasting of Heracleum sosnowskyi habitat suitability under the climate change on publicly available data

This research aims to establish the possible habitat suitability of Heracleum sosnowskyi (HS), one of the most aggressive invasive plants, in current and future climate conditions across the territory of the European part of Russia. We utilised a species distribution modelling framework using publicly available data of plant occurrence collected in citizen science projects (CSP). Climatic variables and soil characteristics were considered to follow possible dependencies with environmental factors. We applied Random Forest to classify the study area. We addressed the problem of sampling bias in CSP data by optimising the sampling size and implementing a spatial cross-validation scheme. According to the Random Forest model built on the finally selected data shape, more than half of the studied territory in the current climate corresponds to a suitability prediction score higher than 0.25. The forecast of habitat suitability in future climate was highly similar for all climate models. Almost the whole studied territory showed the possibility for spread with an average suitability score of 0.4. The mean temperature of the wettest quarter and precipitation of wettest month demonstrated the highest influence on the HS distribution. Thus, currently, the whole study area, excluding the north, may be considered as s territory with a high risk of HS spreading, while in the future suitable locations for the HS habitat will include high latitudes. We showed that chosen geodata pre-processing, and cross-validation based on geospatial blocks reduced significantly the sampling bias. Obtained predictions could help to assess the risks accompanying the studied plant invasion capturing the patterns of the spread, and can be used for the conservation actions planning.