Report on relationships between soil microbial communities and pathways and GHG fluxes

Towards climate-smart sustainable management of agricultural soils SUstainable Management of soil Organic Matter to MItigate Trade-offs between C sequestration and nitrous oxide, methane and nitrate losses Deliverable 3.5 Due date of deliverable: M42 Actual submission date: 26.07.2024 Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 2 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 GENERAL DATA Grant Agreement: 862695 Project acronym: EJP SOIL Project title: Towards climate-smart sustainable management of agricultural soils Project website: www.ejpsoil.eu Start date of the project: February 1st, 2020 Project duration: 60 months Name of lead contractor: INRAE Funding source: H2020-SFS-2018-2020 / H2020-SFS-2019-1 Type of action: European Joint Project COFUND DELIVERABLE NUMBER: 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes DELIVERABLE TITLE: Report on relationships between soil microbial communities and pathways and GHG fluxes. DELIVERABLE TYPE: Report WORK PACKAGE N: 3 WORK PACKAGE TITLE: Filling knowledge gaps with targeted measurements DELIVERABLE LEADER: CSIC and UBLF AUTHORS: Felipe Bastida, Marjetka Suhadolc, Anton Govednik, Hana Flajnik, Stefano Mocali, Ulises Esparza-Robles, Eugenio Díaz-Pinés DISSEMINATION LEVEL: Internal Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 3 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 ABSTRACT Microbes play a significant role in the decomposition of soil organic matter and the release of greenhouse gases (i.e., CO2, CH4, N2O) into the atmosphere. However, the relationship between different components of the soil microbial community, such as biomass and enzyme activities, and the production of these gases is not well understood. Moreover, the association between these microbial properties and greenhouse gas production during soil drying and rewetting cycles is also not sufficiently known, which is critically important given the expected climate change scenarios for the coming decades. In this study, we collected soils from different long-term experiments (LTEs) in Europe and subjected them to a pre-equilibration phase. We then analyzed various components of the soil microbial communities and related them to the production of N2O and CO2 during drying and rewetting periods. Our results show a potential role of the composition and biomass of microbial communities (e.g., fungal-tobacterial biomass ratio and functional gene abundances) in the production of these important greenhouse gases, which opens the door to better understanding microbial processes in the context of climate change. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 4 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Table of Contents List of acronyms and abbreviations ....................................................................... 4 1. Experimental design and methodology ........................................................... 5 2. Results and Discussion..................................................................................... 8 2.1. Soil greenhouse gas fluxes ........................................................................................... 8 2.2. Soil microbial communities and soil enzymatic activities ........................................... 9 2.3. Correlations between soil N2O and CO2 fluxes and soil microbial and nutrient parameters ........................................................................................................................... 11 2.4. Relationship between microbial and biochemical variables and N2O and CO2 production ............................................................................................................................ 14 2.5. Relationships between GHGs production and microbial biomass C and N during the different drying and rewetting stages .................................................................................. 19 2.6. Relationships between GHGs production and N-functional gene abundances ........ 20 2.7. Field case study - relationships between N2O seasonal emissions and dynamics of Nfunctional gene abundances ................................................................................................ 30 3. Conclusions .................................................................................................... 34 4. References ..................................................................................................... 35 List of acronyms and abbreviations LTE: long term experiment GHG: greenhouse gases DR: drying period RW: rewetting period FA: fatty acid Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 5 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 1. Experimental design and methodology Eight long term experiments (LTE) were used in this work, across a network largely covering the pedoclimatic variability in Europe (Latitude range: 40 to 63 °N; longitude range: 3 °W to 24 °E). All LTEs are reported in the MTE/LTE metadataset from EJP SOIL. A full description of all sites can be found in https://doi.org/10.5281/zenodo.7598122, a list of the sites used in presented here (Table 1) with the LTE-Index for identification. All LTEs allowed to compare different soil management strategies. The oldest LTE was established in 1977 and the youngest in 2012. Table 1: List of long-term experiments (LTE) used in this work LTE Name Country LTEIndex Climate Soil management strategies investigated ACBB - Estrées-Mons France 228 Atlantic central Tillage, crop residues Foggia Italy 166 Mediterranean south Crop residues Grabow 1 Poland 289 Continental Organic matter inputs La Poveda Spain 111 Mediterranean north Organic matter inputs, biochar Rutzendorf_17 Austria 138 Pannonian Crop residues Senes Spain 106 Mediterranean south Organic matter inputs Toholampi Finland 187 Boreal Organic matter inputs ULBFLjubljana Slovenia 281 Alpine south Organic matter inputs, Tillage Note: LTE-Index refer to the index used in https://doi.org/10.5281/zenodo.7598122. Climate refers to the climate classification from Metzger et al 2005 (https://doi.org/10.1111/j.1466-822X.2005.00190.x). The individual LTEs may investigate more soil management strategies that have not been considered in this study. The LTEs were investigated to evaluate the relationship between specific soil microbial properties and the emission of N2O and CO2 according to the different soil management strategies. Intact soil samples were collected by the site managers of the respective LTEs and shipped to Vienna for further analyses. In the laboratory, the soils were subjected to a pre-incubation phase so that they could equilibrate. After the pre-incubation phase, soils were subjected to a period of drying (DR) conditions and, afterwards, to an intense rewetting (RW). A subset of samples was subjected to constantly moist conditions along the whole incubation period, meaning that the soil moisture was kept constant by regularly adding water. This treatment allows for the calculation of resistance and resilience indices (de Vries et al. 2012) for different microbial parameters. A scheme of the incubation procedure can be seen in Figure 1. For better between-site comparisons, all soil cores were incubated at 20 °C. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 6 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 1. Schematic view of the laboratory incubation. During the pre-incubation phase, all samples were equilibrated to reach 70 % of total available water content (AWC) and kept for five days under these conditions. After this phase, soil samples from the drying-rewetting treatment (solid black line) underwent a drying-phase lasting for ten days, after which the soil cores were rewetted and. The dashed blue line corresponds to the “constantly moist” treatment and were kept at 70 % AWC during the whole incubation. Soil-headspace exchange of CO2 and N2O was performed at sub-daily resolution from day 0 to 15. Yellow starts indicate the destructive sampling of soil cores: 1) pre-equilibration phase, where PLFA, enzymatic activity, functional genes, soil nutrients and microbial biomass were investigated; 2) drying phase, where soil nutrients and microbial biomass were investigated; and 3) rewetting phase, where soil nutrients and microbial biomass were investigated. By the end of pre-equilibration phase, we estimated the composition of the microbial community by quantification of membrane fatty acids of microbial origin. The extraction of the fatty acids is based on Schutter and Dick (2000). Following microbial groups were considered according to different fingerprinting fatty acids: gram + bacteria (i15:0, a15:0, i16:0, i17:0, 10Me16:0, 10Me18:0); gram – bacteria (16:1ω9, cy17:0, cy19:0); actinobacteria (10Me16:0, 10Me18:0) and fungi (18:2w6,9t, 18:2w6,9c). For further details on identification, chromatography and quantification we refer to Siles et al. (2023). We also estimated soil enzyme activities related to the C cycle (b-glucosidase, following Eivazi and Tabatabai, 1988), to the N cycle (urease, following Kandeler and Gerber, 1988) and to the P cycle (phosphatase, following Eivazi and Tabatabai, 1977). In addition to the investigations of the microbial community and the enzymatic activity, we estimated soil microbial biomass and nutrients at the end of the pre-incubation phase, of the DR phase and of the RW phase. The concentration of total dissolved organic carbon, total dissolved nitrogen, ammonium (NH4+) and nitrate (NO3-) were estimated in soil extracts. Concentration of NH4+ in the soil extracts was estimated with the green indophenol method (Verdouw et al., 1978) using a spectrophotometer at a wavelength of 660 nm. Analysis for NO3was done via reduction with vanadium, with development of color (540 nm) using sulfanilic acid and naphthyl ethylenediamine (Pelster et al. 2017). Soil microbial biomass C and N was estimated with the fumigation-extraction Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 7 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 technique (Brookes et al., 1985). All nutrient analyses were performed in fumigated and nonfumigated samples; no extraction coefficient was used. Functional genes abundance was also investigated at the end of the per-incubation phase. Soil DNA was extracted using a DNeasy PowerSoil Pro Kit (Qiagen, Venlo, Netherlands). Target genes were bacterial 16S rRNA, fungal ITS, thaumarchaeal 16S rRNA, nirK (Cu-containing nitrite reductase), nirS (nitrite reductase), nosZ clade I and nosZ clade II (nitrous oxide reductase), amoA (bacterial ammonium nonooxygenase, amoA (archaeal ammonium monooxygenase), and nrfA (pentaheme c-type cytochrome nitrite reductase). Ratios to illustrate relative abundances and to identify dominant genes within processes were used. Further details can be found in Govednik et al. (2024). The pre-incubation phase was followed by a 15-day-gas-analysis period, encompassing the drying and the rewetting period (Figure 1). During this period, intact soil cores were investigated for fluxes of CO2 and N2O using an automated incubation system. The system is based on dynamic chamber measurements (Steady-state through-flow chambers, according to (Pumpanen et al., 2004). The system is based on the one developed by Schindlbacher et al. (2004) with some adaptations and has been already used in similar studies (e.g. Ferretti et al. 2024). The system can host up to 22 chambers, each containing an intact soil core, plus two additional chambers without soil that are used as a reference. Temperature was set to 20 °C and the flow rate to 300 ml min-1. The automated system was coupled to a G2301 and a G5131i gas analyser for CO2 and N2O detection, respectively (PICARRO, Inc., Santa Clara, USA). The chambers were continuously flushed to avoid anomalous accumulation of greenhouse gases in the headspace. The continuous flushing also allowed for a rapid achievement of the steady state concentration once a chamber was switched to be measured by the analysers. The flux estimation procedure involved the a six-minute measurement of a soil chamber followed by a four-minute measurement of a reference chamber. Roughly, we gained an estimation of the CO2 and N2O fluxes every four hours. Fluxes were temporally aggregated into daily values. Here, average soil fluxes during the drying phase (day 1 to 10) and during the rewetting phase (day 10 to 15) are presented. We further present results showing the relationships between the bacterial and fungal community (through fatty acids assessment) and the N2O and CO2 fluxes, as estimated during either the drying phase (DR) and the rewetting phase (RW). Further, we analyze the correlation between microbial biomass C and microbial biomass N with GHGs during the respective DR and RW phases. Pearson and Spearman correlations were calculated in order to evaluate lineal or non-linear relationship between variables. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 8 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 2. Results and Discussion 2.1. Soil greenhouse gas fluxes Results of soil N2O fluxes are presented from selected sites in Figures 2 and 3. A high variability between LTEs was observed. During the drying period, N2O fluxes were generally low; only samples from the Spanish LTE La Poveda (where compost and biochar has been added in the field) showed larger N2O fluxes than the rest (Figure 2). Our preliminary results suggest an interaction between biochar and organic matter addition on the soil N2O response after rewetting (Figure 3). In the Slovenian LTE, addition of mineral Nitrogen seems to enhance soil N2O emissions compared to the addition of compost, while no tillage seems to further decrease N2O fluxes. Figure 2. Mean (± standard error) cumulative soil N2O fluxes (µg N2O-N m-2) during the drying period for selected sites. “Control” denotes either removal of crop residues (for Rutzendorf) or mineral fertilization (for the rest of the sites). “NT” denotes no tillage, “B-“ denotes no addition of biochar and “B+” denotes addition of biochar respectively. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 9 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 3. Mean (± standard error) cumulative soil N2O fluxes (µg N2O-N m-2) during the rewetting period for selected sites. “Control” denotes either removal of crop residues (for Rutzendorf) or mineral fertilization (for the rest of the sites). “NT” denotes no tillage, “B-“ denotes no addition of biochar and “B+” denotes addition of biochar respectively. 2.2. Soil microbial communities and soil enzymatic activities A summary of microbial community composition according to the PLFA and of the enzymatic activity data of the investigated LTEs and the treatments considered in each of them is shown in Table 2. Here, we provide a visual interpretation of the patterns observed. As expected, a large variability was observed across sites. Values of total PLFA of microbial origin were relatively low in LTE 138 Rutzendorf, LTE 166 Foggia and LTE 228 Estrées-Mons (54.6, 86,6 and 95.1 nmol FAMES g-1 dw, respectively) compared to the rest of the LTEs, that ranged between 150 and 200 nmol FAMES g-1 dw. Fungal to bacterial ratio ranged from 0.1 (LTE 187, Toholampi) and 0.27 (LTE 111, La Poveda). Enzymatic activity was also highly variable across sites. Toholampi show the lowest potential urease and phosphatase activity (0.16 µmol NH4+-N g-1 h-1 and 0.74 µmol PNP g-1 h-1, respectively); lowest values for β-glucosidase were shown in Rutzendorf and Foggia (0.76 and 0.88 µmol PNP g-1 h-1, respectively). Rutzendorf was also the LTE with the highest potential urease activity 7.78 µmol NH4+-N g-1 h-1 , Ljubljana had the highest values for phosphatase (8.00 µmol PNP g-1 h-1) and β-glucosidase (1.99 µmol PNP g-1 h-1). When looking at the effect of soil management strategies on microbial properties, the results suggest an interaction with site and/or pedoclimatic conditions. Noor reduced tillage Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 16 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 7. Relationship between microbial properties and N2O production during rewetting. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 17 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 8. Relationship between microbial properties and CO2 production during drying. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 18 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 9. Relationship between microbial properties and CO2 production during rewetting Figure 10. Relationship between the fungal-to-bacterial biomass ratio with N2O and CO2 production during drying (A) and rewetting (B), and with CO2 production during drying (C) and rewetting (D) Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 19 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 2.5. Relationships between GHGs production and microbial biomass C and N during the different drying and rewetting stages Microbial biomass C and N were additionally measured during the DR and RW periods. Here, we show the type of association (Fig 11 and 12) between these variables with CO2 and N2O production. Overall, results point to a clear quadratic relationship between the microbial biomass C and CO2 production both in DR and RW periods. However, the relationship between microbial biomass N and N2O were weaker. Figure 11. Relationship between microbial biomass C and CO2 production during drying (A) and rewetting (B) Figure 12. Relationship between microbial biomass N and N2O production during drying (A) and rewetting (B) Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 20 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 2.6. Relationships between GHGs production and N-functional gene abundances Similar correlation patterns across CO2 and N2O average production rates and functional gene abundances and ratios were found (Fig 13), however there is a difference in the number of significant correlations between both gases and the type of treatment (DR vs RW). Average CO2 production during the drying event (CO2_DR) exhibited the highest number of correlations. It was positively correlated to AOB/16S, nosZI/nosZII, nirS/16S and nosZI/16S, and negatively to total extracted DNA, absolute abundances of bacterial 16S, nrfA, nosZII, nirK and ratios nrfA/16S, nirK/nirS, AOA/AOB, nirK/16S and nosZII/16S. More detailed relationships with the mentioned variables are presented on Fig 14 and 16. Production of CO2 during the rewetting cycle (CO2_RW) followed the same pattern regarding the negative correlations with mentioned gene ratios (Fig 17), however without significant correlations with absolute gene abundances (Fig 15). Interestingly, average N2O production rates in both drying and rewetting cycles exhibited similar patterns to CO2 respiration in respective drying and rewetting cycle, however with lower number of correlations. Production of N2O during a drying cycle (N2O_DR) was therefore positively correlated with nosZI/nosZII, nirS/16S, nosZI/16S and negatively with nrfA/16S, nirK/nirS, AOA/AOB, nirK/16S and nosZII/16S, while N2O production during rewetting cycle (N2O_RW) exhibited positive correlation with nirS/16S and negative with nrfA/16S and nirK/nirS. More detailed representation of relationships is shown on Fig 18 to 21. Overall, we can see that CO2 production shows stronger correlation patterns with microbial community abundances such as 16S, nrfA, nirK but also total DNA than N2O production which was expected as higher abundances indicate higher microbial biomass and therefore potential for CO2 respiration. While CO2 production is more of a continuous process, N2O production is more stochastic and dependent on suitable environmental variables such as anaerobicity, increased water content etc. (Butterbach Bahl et al., 2013). This increases difficulty of correlating functional genes’ abundance with N2O production (Rocca et al., 2015). Nevertheless, we could observe negative correlation between N2O_DR and nosZII/16S gene ratio which has been in shown in the past to correlate with increased potential for N2O sink (Domeignoz-Horta et al., 2016). In the drying period we would expect higher contribution of nitrification to N2O emissions due to a decreasing water content, however the correlations with bacterial nitrifiers (AOB) were not significant or were even negative (AOA). Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 21 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 13. Correlation matrix of abundance of marker genes for total bacterial (16S), DNRA (nrfA), nitrifying (AOA, AOB) and denitrifying (nirK, nirS, nosZI, nosZII) community, their respective ratios, and CO2 and N2O production in drying (DR) and rewetting (RW) cycles. Numbers indicate Spearman correlation coefficient also emphasized by color scale. Only significant (p < 0.05) correlations are presented. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 22 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 14. Relationship between abundance of marker genes for total bacterial (16S), DNRA (nrfA), nitrifying (AOA, AOB) and denitrifying (nirK, nirS, nosZI, nosZII) community) and CO2 production in drying (DR) cycle. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 23 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 15. Relationship between abundance of marker genes for total bacterial (16S), DNRA (nrfA), nitrifying (AOA, AOB) and denitrifying (nirK, nirS, nosZI, nosZII) community) and CO2 production in rewetting (RW) cycle. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 24 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 16. Relationship between percentage of marker genes within total bacterial community for DNRA (nrfA/16S), nitrifying (AOB/16S) and denitrifying (nirK/16S, nirS/16S, nosZI/16S, nosZII/16S) community, and respective ratios within mentioned communities (AOA/AOB, nirK/nirS, nosZI/nosZII and (nirS+nirK)/(nosZI+nosZII)) and CO2 production in drying (DR) cycle. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 25 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 17. Relationship between percentage of marker genes within total bacterial community for DNRA (nrfA/16S), nitrifying (AOB/16S) and denitrifying (nirK/16S, nirS/16S, nosZI/16S, nosZII/16S) community, and respective ratios within mentioned communities (AOA/AOB, nirK/nirS, nosZI/nosZII and (nirS+nirK)/(nosZI+nosZII)) and CO2 production in rewetting (RW) cycle. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 32 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Figure 24. Smoothed conditional means with local polynomial regression fitting for (a) AOB/16S, (b) nosZI/nosZII and (c) (nirK + nirS)/(nosZI + nosZII) ratios over the 10 sampling dates in no till (NT) and conventional tillage (CT) systems in combination with unfertilized control (CON), mineral (MIN) and organic (ORG) fertilisation regimes. Shaded areas represent 95% confidence intervals. Deliverable 3.5 M42 Report on relationships between soil microbial communities and pathways and GHG fluxes 33 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 Table 4. Variables and respective coefficients with p values of forward selected multiple regression model for explaining N2O cumulative emissions. 3. Conclusions Our results allow us to conclude that relationships between CO2 and N2O production and soil biological variables are non-linear. Overall, results point towards a functional resilience mechanism in the microbial community for which microbes recover after drying to an initial stage similar to the preincubation stage. The composition of the microbial community, roughly estimated through the F/B ratio, was indeed critically in controlling the production of CO2 and N2O, so any agricultural imbalance of the abundance of fungi vs bacteria can potentially alter the release of these GHGs. In contrast, soil enzyme activities had no statistical relationships with production of GHGs and thus are poor predictors of GHGs compared to microbial-biomass related parameters. Functional gene abundances showed correlation with CO2 and N2O production; however, no clear pattern could be distinguished. The production of CO2 showed stronger correlation with more functional genes, probably indicating increased microbial biomass potential for respiration. The stochasticity of N2O production probably influenced the reduced correlation with functional gene abundances and abundance ratios. Taken together, it must be acknowledged that by quantifying gene abundances, we are assessing only the genetic potential of microbial community capable of certain processes. How those processes are activated depends significantly on soil properties and environmental conditions. 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