Revisiting soil fungal biomarkers and conversion factors: Interspecific variability in phospholipid fatty acids, ergosterol and rDNA copy numbers

- Refined conversion factors for soil fungal biomarkers are proposed. - High interspecific variability is present in all fungal biomarkers. - A modeling approach supports the validity of biomarker estimates in diverse soils. - ITS1 copies vary strongly, but are fungal-specific with least phylogenetic bias. - A combination of fungal biomarkers will reveal soil fungal physiology and activity. The abundances of fungi and bacteria in soil are used as simple predictors for carbon dynamics, and represent widely available microbial traits. Soil biomarkers serve as quantitative estimates of these microbial groups, though not quantifying microbial biomass per se. The accurate conversion to microbial carbon pools, and an understanding of its comparability among soils is therefore needed. We refined conversion factors for classical fungal biomarkers, and evaluated the application of quantitative PCR (qPCR, rDNA copies) as a biomarker for soil fungi. Based on biomarker contents in pure fungal cultures of 30 isolates tested here, combined with comparable published datasets, we propose average conversion factors of 95.3 g fungal C g−1 ergosterol, 32.0 mg fungal C µmol−1 PLFA 18:2ω6,9 and 0.264 pg fungal C ITS1 DNA copy−1. As expected, interspecific variability was most pronounced in rDNA copies, though qPCR results showed the least phylogenetic bias. A modeling approach based on exemplary agricultural soils further supported the hypothesis that high diversity in soil buffers against biomarker variability, whereas also phylogenetic biases impact the accuracy of comparisons in biomarker estimates. Our analyses suggest that qPCR results cover the fungal community in soil best, though with a variability only partly offset in highly diverse soils. PLFA 18:2ω6,9 and ergosterol represent accurate biomarkers to quantify Ascomycota and Basidiomycota. To conclude, the ecological interpretation and coverage of biomarker data prior to their application in global models is important, where the combination of different biomarkers may be most insightful

Revisiting soil fungal biomarkers and conversion factors: Interspecific variability in phospholipid fatty acids, ergosterol and rDNA copy numbers

- Refined conversion factors for soil fungal biomarkers are proposed. - High interspecific variability is present in all fungal biomarkers. - A modeling approach supports the validity of biomarker estimates in diverse soils. - ITS1 copies vary strongly, but are fungal-specific with least phylogenetic bias. - A combination of fungal biomarkers will reveal soil fungal physiology and activity. The abundances of fungi and bacteria in soil are used as simple predictors for carbon dynamics, and represent widely available microbial traits. Soil biomarkers serve as quantitative estimates of these microbial groups, though not quantifying microbial biomass per se. The accurate conversion to microbial carbon pools, and an understanding of its comparability among soils is therefore needed. We refined conversion factors for classical fungal biomarkers, and evaluated the application of quantitative PCR (qPCR, rDNA copies) as a biomarker for soil fungi. Based on biomarker contents in pure fungal cultures of 30 isolates tested here, combined with comparable published datasets, we propose average conversion factors of 95.3 g fungal C g−1 ergosterol, 32.0 mg fungal C µmol−1 PLFA 18:2ω6,9 and 0.264 pg fungal C ITS1 DNA copy−1. As expected, interspecific variability was most pronounced in rDNA copies, though qPCR results showed the least phylogenetic bias. A modeling approach based on exemplary agricultural soils further supported the hypothesis that high diversity in soil buffers against biomarker variability, whereas also phylogenetic biases impact the accuracy of comparisons in biomarker estimates. Our analyses suggest that qPCR results cover the fungal community in soil best, though with a variability only partly offset in highly diverse soils. PLFA 18:2ω6,9 and ergosterol represent accurate biomarkers to quantify Ascomycota and Basidiomycota. To conclude, the ecological interpretation and coverage of biomarker data prior to their application in global models is important, where the combination of different biomarkers may be most insightful

The mineralosphere—interactive zone of microbial colonization and carbon use in grassland soils

To improve our understanding of early microbial colonization of pristine minerals and their group-specific C utilization, we exposed minerals (illite/goethite/quartz) amended with artificial root exudates (ARE, glucose, and citric acid) in grassland soils for a period of 24 weeks. FTIR spectra indicated that mineral-associated ARE were used within the first 2 weeks of exposure and were replaced by other carbohydrates derived from living or dead cells as well as soil-borne C sources transported into the mineralosphere after heavy rain events. Fungi and Gram-positive bacteria incorporated ARE-derived C more rapidly than Gram-negative bacteria. Gram-negative bacteria presumably profited indirectly from the ARE by cross-feeding on mineral-associated necromass of fungi and Gram-positive bacteria. The Gram-negative bacterial phyla Verrucomicrobia, Planctomycetes, Gemmatimonadetes, Armatimonadetes, and Chloroflexi showed a positive correlation with Gram-negative PLFA abundances. After 24 weeks of exposure in the grassland soils, abundances of soil microorganisms in the mineralosphere reached only 3.1% of the population density in soil. In conclusion, both bacteria and fungi slowly colonize new surfaces such as pristine minerals, but quickly assimilate artificial root exudates, creating an active microbial community in the mineralosphere

Towards establishing a fungal economics spectrum in soil saprobic fungi

Trait-based frameworks are promising tools to understand the functional consequences of community shifts in response to environmental change. The applicability of these tools to soil microbes is limited by a lack of functional trait data and a focus on categorical traits. To address this gap for an important group of soil microorganisms, we identify trade-offs underlying a fungal economics spectrum based on a large trait collection in 28 saprobic fungal isolates, derived from a common grassland soil and grown in culture plates. In this dataset, ecologically relevant trait variation is best captured by a three-dimensional fungal economics space. The primary explanatory axis represents a dense-fast continuum, resembling dominant life-history trade-offs in other taxa. A second significant axis reflects mycelial flexibility, and a third one carbon acquisition traits. All three axes correlate with traits involved in soil carbon cycling. Since stress tolerance and fundamental niche gradients are primarily related to the dense-fast continuum, traits of the 2nd (carbon-use efficiency) and especially the 3rd (decomposition) orthogonal axes are independent of tested environmental stressors. These findings suggest a fungal economics space which can now be tested at broader scales.peerReviewe

Camenzind et al._Fungal trait data and code

This table includes all trait data presented in the manuscript "Ecological dimensions of the fungal economics spectrum in soil saprobic fungi"