Differences in thallus chemistry are related to species‐specific effects of biocrust‐forming lichens on soil nutrients and microbial communities

Abstract

Summary 1.It is well-known that vascular plants have species-specific effects on soil properties. However, little is known on how individual species forming biocrusts, communities dominated by lichens, mosses and cyanobacteria that are prevalent in many ecosystems world-wide, affect microbial communities and soil variables related to nutrient cycling. 2. We evaluated the relationship of six biocrust-forming lichens (Buellia epipolia, Diploschistes diacapsis, Fulgensia subbracteata, Psora decipiens, Squamarina cartilaginea and Squamarina lentigera) with microbial abundance and multiple variables associated with soil nitrogen (N), carbon (C) and phosphorus (P) cycling and storage. We also evaluated whether the composition of lichen tissues (contents in C, N, P and polyphenols) is related to the C, N, P availability and microbial abundance in soils. Finally, we assessed what lichen species positively and negatively relate to soil fertility compared to bare ground areas without biocrusts. 3. We found contrasted C, N, P availability and soil microbial abundance under the different biocrust-forming lichens. Interestingly, inorganic P and amino acids were the most important factors differentiating lichen microsites. These differences in nutrient availability seem to be related to the C, N and P composition of the lichen tissues. For example, soils under D. diacapsis and P. decipiens, which had the lowest and highest C, N and P contents in their tissues, respectively, had the lowest and highest nutrient availability, respectively. We also found contrasted soil microbes abundance under the different soil lichens. For instance, F. subbracteata and D. diacapsis were negatively related to the abundance of bacteria compared to bare ground areas. 4. Our results support the idea that, as found with vascular plants, biocrust-forming lichens have species-specific effects on soil microbial communities and C, N and P cycling. Thus, continuing considering biocrusts as a unique entity will only add confusion to our knowledge of how they control nutrient availability and microbial abundance in the ecosystems where this key community is prevalent.

Species identity of biocrust-forming lichens drives the response of soil nitrogen cycle to altered precipitation frequency and nitrogen amendment

“…For example, although we did not measure N in lichen tissue in this study, Delgado-Baquerizo et al (2015) reported that species such as Diploschistes diacapsis (similar to D. thunbergianus here) had the lowest N availability in thalli tissue and soil among the six examined species from the Iberian peninsula, whereas Psora decipiens (similar to P. crystallifera) contained the highest N content. Soil microsites with high N availability for plants have been reported to reduce N 2 O fluxes (Zaman et al, 2009;Liu et al, 2013), likely by promoting AOA and AOB communities, which could explain the lowest N 2 O flux rates under P. crystallifera and X. reptans microsites (Table 1).…”

“…lichen species), and their associating microbial communities, modulate the impact of global change drivers on nutrient cycling. A recent study suggested that, similar to vascular plants, biocrust-forming lichens have species-specific effects on soil nutrient cycling and microbial abundance (Delgado-Baquerizo et al, 2015). However, the role of different biocrust-forming species in controlling the response of nutrient cycling (here N transformation processes) to global environmental change remains unresolved.…”

Microbial regulation of the soil carbon cycle: evidence from gene–enzyme relationships

“…Because of the significant number of microbial predictors (that is, relative abundance of bacteria and fungi and functional genes), we used a classification Random Forest (RF) analysis (Breiman, 2001) as explained in Delgado-Baquerizo et al (2015) to identify the main microbial predictors of extracellular enzyme activities. The main goal with this analysis is to reduce the number of predictors for further modelling (structural equation modelling).…”

“…rproject.org/web/packages/rfPermute/rfPermute.pdf) package for R. After this, and owing to the correlative nature of our data, structural equation modelling (SEM) was used to identify the relative importance and effects of functional genes vs abiotic factors (total C and pH) and microbial composition on soil function (that is, enzyme activities). Unlike regression or analysis of variance, SEM offers the ability to separate multiple pathways of influence and view them as a system (Shipley, 2002;Grace, 2006;Delgado-Baquerizo et al, 2015). Another important capability of SEM is its ability to partition direct and indirect effects that one variable may have on another and estimate the strengths of these multiple effects (Shipley, 2002;Grace, 2006;Delgado-Baquerizo et al, 2015).…”

“…Unlike regression or analysis of variance, SEM offers the ability to separate multiple pathways of influence and view them as a system (Shipley, 2002;Grace, 2006;Delgado-Baquerizo et al, 2015). Another important capability of SEM is its ability to partition direct and indirect effects that one variable may have on another and estimate the strengths of these multiple effects (Shipley, 2002;Grace, 2006;Delgado-Baquerizo et al, 2015). SEM was generated based on the known effects and relationships among key drivers of microbial community composition and function (Supplementary Figure S1).…”