Microbial communities associated with decomposing deadwood of downy birch in a natural forest in Khibiny Mountains (Kola Peninsula, Russian Federation)

Forest Ecology and Management 455 (2020) 117643

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Microbial communities associated with decomposing deadwood of downy birch in a natural forest in Khibiny Mountains (Kola Peninsula, Russian Federation)

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Roberta Pastorellia, , Alessandro Palettob, Alessandro E. Agnellia, Alessandra Lagomarsinoa, Isabella De Meoa ⁎

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Research Centre for Agriculture and Environment, Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA-AA), via di Lanciola 12/A, 50125 Firenze, Italy Research Centre for Forestry and Wood, Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria (CREA-FL), piazza Nicolini 6, 38100 Trento, Italy

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ARTICLE INFO

ABSTRACT

Keywords: Deadwood Decay classes Microbial diversity Microbial abundance Climate change Khibiny Mountains

Deadwood plays an important role in the forest ecosystems, providing nutrients and habitat for a wide range of organisms, preventing soil erosion, and improving carbon storage. Microorganisms are primary agents in wood decomposition. The aim of the present research is to describe the changes in diversity, structure and abundance of microbial communities over downy birch lying deadwood decomposition in a boreal forest under natural conditions. This study also included investigations on the potential involvement of deadwood in climate change. The decomposition of deadwood was visually assessed using a five-class system. The microbial community diversity and composition were assessed with polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting. Real time PCR was used to evaluate the absolute and relative microbial abundance. The potential involvement of deadwood in climate change was assessed by in situ-measuring of the carbon dioxide (CO2) emissions from downy birch lying deadwood and from soil. The results obtained indicate that deadwood represents a substrate whose physico-chemical and microbiological property change with time. Higher diversity of fungi, bacteria, and archaea were recorded in the decay class 5. Conversely, actinobacteria showed the lowest values of diversity in decay class 5. We observed a succession of dominant microbial taxa over the decomposition progress. Overall, the abundance of each microbial group increases with the advance of decomposition. Among the estimated physico-chemical properties, nitrogen content, that increased with decay, and pH were the most important candidate drivers of microbial community composition and abundance. CO2 emissions were recorded higher in the decay class 5 and in soil of plots with the highest amount of lying deadwood. Bacteria dominated the microbial community and may play a more important role in the late stages of wood decomposition conversely to fungi and actinobacteria that are assumed to be primary involved in early stages of wood colonization. Archaea are shown to be an integral and dynamic component of decaying wood biota. The presence of large amounts of deadwood may affect greenhouse gas (GHG) emissions, especially in the event of an increase in temperatures that could reduce the carbon (C) sink capacity of the boreal forests.

1. Introduction

saprotrophic and heterotrophic organisms (Köster et al., 2015). Deadwood constitutes a dynamic habitat evolving with time, with changes in its physical and chemical characteristics (Lachat et al., 2013). These temporal and spatial changes are crucial in preserving deadwood-dependent species that must be able to adapt to the new suitable habitats or find new habitats to not extinguish. Although it remains largely unclear which determinants influence which group of organisms and to what extent, there is no doubt that deadwood offers a

In the last decades, the decision makers’ awareness of the importance of deadwood in forest has increased, through the recognized fundamental role that it plays in many ecosystem functions. Deadwood contributes to nutrient cycling, humus and soil formation, carbon storage (Liu et al., 2013), it facilitates tree regeneration (Hekkala et al., 2016; Błońska et al., 2017), and it provides habitat for a wide range of

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Corresponding author. E-mail address: [email protected] (R. Pastorelli).

https://doi.org/10.1016/j.foreco.2019.117643 Received 8 July 2019; Received in revised form 9 September 2019; Accepted 18 September 2019 0378-1127/ © 2019 Elsevier B.V. All rights reserved.

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wide range of niches for many specialized organisms (Moll et al., 2018). Among the range of organisms which found their habitat in deadwood, microorganisms may be considered keystone species, given their active involvement in wood decay (Moll et al., 2018). Wood decomposition affects carbon and nutrient retention providing resources available for other biota, such as plants. Therefore, wood decomposition largely contributes to sustain forest biodiversity (Rock et al., 2008). For this reason, a deeper knowledge of the microbial diversity and activity in deadwood should be considered, in order to maximize our understanding of forest ecosystem functions and biodiversity conservation strategies (Lachat et al., 2013). Fungi are considered pioneering players of deadwood decomposition, due to the ability to secrete various enzymes that efficiently break down wood biopolymers (cellulose, hemicellulose and lignin) (Hoppe et al., 2016). Thanks to this ability, fungi colonize wood and use the nutrients for their growth (Baldrian et al., 2016). In recent years, it has been shown that also bacteria play an important role in deadwood degradation, albeit to a lesser extent than fungi. Bacteria remain poorly investigated compared to fungi, probably due to their limited ability in decomposing wood and cell wall polymers and to colonize plant tissue (Bani et al., 2018). Nevertheless, there was recently highlighted the occurrence of bacteria in degrading lignin and catabolize side products deriving from wood incomplete degradation by fungi (Kielak et al., 2016). Several studies indicate that fungi and bacteria can co-occur, interact with and influence each other in the decay processes. By degrading wood polymers, fungi modify lying deadwood weakening lignin barriers and releasing easily degradable oligomers thus providing opportunities for bacterial cells to access and growth. Concurrently, also bacteria with their activity in the earliest stages of decay may make wood more accessible to fungi (Frey-Klett et al., 2011). On the other hand, fungal decomposition processes lower the pH and generate reactive oxygen species, creating a harsh and selective environment for bacterial colonization (Johnston et al., 2016; Kielak et al., 2016). Bacteria may have negative effect on fungal community by competing for nutrients and energy, but they may also provide fungi with limiting nutrients such as nitrogen via nitrogen fixation (Gómez-Brandón et al., 2017), or growth factors like vitamins (Kielak et al., 2016) and may detoxify certain compounds inhibitory to fungal growth (Johnston et al., 2016). Thus, as woody material decays, the occurrence of chemical and structural modifications reflects in a succession of fungal and bacterial taxa with different decaying abilities and more suited to the new substrates or to survive via competitiveness. However, few studies have explored changes in fungal and bacterial community composition during wood decay, especially in boreal forests. In particular, the succession of other microbial groups, such as archaea and actinobacteria, during deadwood decomposition is not yet well-studied. In North-European natural boreal forests, the amount of deadwood varies between 60 and 80 m3 ha−1, in reason of diverse latitude and altitude (Siitonen, 2001; Hahn and Christensen, 2004). Since the slow decay rate that is assumed to occur in this climate (Siitonen, 2001; Bhupinderpal et al., 2003; Olsson et al., 2005), deadwood may serve as a carbon (C) pool for up to many decades (Sandström et al., 2007; Pan et al., 2011). Thanks to this assumption, the northern boreal forests represent the major terrestrial carbon sink (Olsson et al., 2005) and in Russia they cover a very large area, accounting for over 20% of the world’s forest area and about 50% of all boreal forests (Krankina et al., 1996; Pan et al., 2011). According to the Kyoto Protocol, C sequestration in terrestrial sinks can be used to offset greenhouse gas (GHG) emissions (IPCC, 2006). However, recent warming is greatly affecting decomposition processes in boreal forest ecosystems, with positive feedbacks to climate change. In fact, the presence of deadwood in forest ecosystems and its decomposition processes also play an important issue in GHG relate climate change research activities (Köster et al., 2015). Moreover, the efflux of carbon dioxide (CO2) from the soil (soil respiration) is considered a

major component of CO2 exchange between forest ecosystems and the atmosphere. Boreal soils generally contain a much greater share of C than the vegetation (Olsson et al., 2005; Laganière et al., 2012), that it may account for more than two-third of the CO2 released through ecosystem respiration and about one-half of the CO2 assimilated through gross ecosystem photosynthesis (Gaumont-Guay et al., 2006). For this reason, soil respiration has a significant impact on the CO2 sink strength of forest ecosystems and future atmospheric CO2 concentrations. The main focuses of the present study were i) to describe the succession of the microbial taxa during wood decay and ii) to evaluate the potential involvement of the deadwood in climate change. The investigations were conducted in a boreal forest under natural condition located in the Kola Peninsula (northwestern of Russian Federation), targeting our attention on downy birch lying deadwood. The composition of microbial communities (fungi, bacteria, actinobacteria, and archaea) was assessed by using the polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting method. The abundance of these four microbial groups was evaluated by real time PCR using both absolute and relative procedures. DGGE has been extensively used to study microbial community composition in various environments and although this method tends to bias towards the predominant microbial groups within a community (Kan et al., 2006), it may as well provide indications on the relative changes in abundance and genetic community structure between samples. The C stock and the potential decay rate of deadwood in forest ecosystem was assessed by measuring C concentration and the CO2 production in situ downy birch lying deadwood and by evaluating the soil respiration depending on deadwood volume distribution. 2. Methods 2.1. Study area The study area is the Khibiny Mountains (67° 41′ N, 033° 15′ E) a compact low-elevation system located in the central part of the Kola Peninsula in the northwestern of Russian Federation (Kremenetski et al., 1999). The maximum elevation of the Khibiny Mountains is about 1250 m a.s.l. in the highest point of the Kola Peninsula. The geology is represented mainly by Precambrian crystalline rocks related to the Fennoscandian crystalline massif. Khibiny Mountains are characterized by flat plateau and steep slopes, mountains develop around two large lakes: Imandra Lake (128 m a.s.l.) from the west and Umbozero (Umbyavr) Lake (151 m a.s.l.) from the east. With regard to the soil, the many-humus illuvial podzol soils of the mountain forests and the podbur soils of the mountain tundra are formed in this area on alkaline nephelin sienits that are rich in minerals (Zenkova and Rapoport, 2013). The climate is cold-temperate, but the orographic effects of the mountain massif influence the local conditions of the area. In fact, the Khibiny Mountains are characterized by the coldest winters and the highest amount of precipitation for the entire Kola Peninsula (Kremenetski et al., 1999). The mean temperature in January at the southern/southwestern margin of the Khibiny is about −12 °C, while the mean temperature in July is in the range of +13 °C. The annual precipitation amounts to 450 mm, 75–120 mm of it falling during winter (Apatity station). The midday position of the sun above horizon equals zero during the polar night in December and it is maximal in June and July. The Khibiny Mountains are located in the northern part of the boreal forest belt and are characterized by mountain taiga and tundra vegetation (Kononov et al., 2009). The main forest types in Khibiny Mountains are pure downy birch (Betula pubescens subsp. tortuosa (Ledeb.) Nyman) forests and downy birch forests mixed with Siberian spruce (Picea abies ssp. obovata (Ledeb.) Hultén). The secondary tree species in these forest types are: Scots pine (Pinus sylvestris L.), rowan 2

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(Sorbus aucuparia subsp. glabrata (Wimm. & Grab.) Hedl.), Eurasian aspen (Populus tremula L.), white willow (Salix alba L.), goat willow (Salix caprea L.), and French willow (Salix trianda L.).

The deadwood samples were coarsely ground in a cutting mill (Retsch SM 100, Haan, Germany), at rotor speed of 1500 rpm min−1, until a final fineness of 0.5 mm. Successively, the genomic DNA was extracted from 0.25 g of sawdust of each sample, by using the FAST DNA SPIN kit for soil (Biomedicals, Santa Ana, CA, USA) according to the manufacturer’s guidelines. DNA was eluted in sterile water and its integrity was verified by agarose gel electrophoresis (0.8% w/v).

triphosphates (dNTPs), 400 nM each primer, 0.4 ng/μl BSA, and 1U GoTaq®Flexi DNA polymerase (Promega). The amplification products were verified by agarose gel electrophoresis (1.2 w/v) and amplicon yields were estimated by comparison of amplified DNA to Low DNA mass ladder (Invitrogen, Carlsbad, CA, USA) using the Chemidoc XRS Apparatus (Bio.Rad). Prior DGGE, three independent PCR amplifications for each sample were performed, and the products were pooled to minimize the effect of PCR biases. The DGGE analysis was carried out using the INGENY phorU-2 System (Ingeny International BV, Goes, The Netherlands). Amplicons (600 ng) were loaded on a polyacrylamide gel (40% acrylamide/bis 37.5:1; Fisher Scientific, Geel, Belgium) containing a linear chemical denaturant gradient obtained with a 100% denaturant solution consisting of 40% v/v deionized formamide (VWR, West Chester, PA, USA) and 7 M Urea (Promega, Madison, WI, USA). Gels were run in a 1X TAE buffer for 17 h at 60 °C and constant voltage (75 V). After electrophoresis, gels were stained with SYBR®Gold (Molecular Probes, Eurogene, OR, USA) diluted 1:1000 in 1X TAE buffer, and images were digitally captured under UV light (λ = 302 nm) using the ChemiDoc XRS apparatus (Bio-Rad). In particular, fungal 18S rDNA amplicons were obtained using EF390-GCFR1 (Vainio and Hantula, 2000) primer pair and DGGE was carried out in an 8% polyacrylamide gel with a 35–64% denaturing gradient; total bacterial 16S rDNA amplicons were obtained using GC986f-UNI1401r (Nübel et al., 1996) primer pair and DGGE was carried out in a 6% polyacrylamide gel with a 45–65% denaturing gradient; actinobacterial 16S rDNA amplicons were obtained using F243-R513GC (Heuer et al., 1997) primer pair and DGGE was carried out in an 8% polyacrylamide gel with a 42–75% denaturing gradient; archaeal 16S rDNA amplicons were obtaining using GC1106F1378 (Watanabe et al., 2006) primer pair and DGGE was carried out in an 8% polyacrylamide gel with a 38–74% denaturing gradient. Evaluation of band migration distance and intensity within each lane of the DGGEs was performed using Gel Compare II software v. 4.6 (Applied Maths, Saint-Martens-Latem, Belgium). Although amplification products coming from the different microbial species can co-migrate, each band was considered to match a single microbial operational taxonomic unit (OTU), and band intensity (relative surface of the peak compared to the surface of all the peaks in the profile) was considered to indicate the relative abundance of the corresponding microbial OTU (Fromin et al., 2002). The number of bands and their relative abundance were used as a proxy of dominant OTU richness (Wang et al., 2011) and diversity (Shannon index) of microbial communities within each DGGE profile, as described by Pastorelli et al. (2011).

2.4. DGGE analysis

2.5. Real-time PCR

PCR reactions were performed using specific primer pairs to amplify the small subunit rRNA (SSR) gene of fungi, bacteria, actinobacteria and archaea (see below for additional details). Amplifications were carried out in a T100 Thermal Cycler (Bio-rad Laboratories, Hertfordshire, UK) in a 25 μL volume containing 1X Flexi PCR buffer (Promega, Madison, WI), 1.5 mM MgCl2, 250 μM deoxynucleotide

Real-time PCR was performed in a MJ Research PTC-200™ Chromo4 thermocycler (Bio-Rad) using optical grade 96-well plates (Starstedt, Montréal, Canada). Amplification reactions were carried out in a 10-μl mixture containing 1X SSoAdvanced Universal SYBR®Green Supermix (Bio-Rad), 200 nM of each primer, 0.4 ng/μl BSA, distilled water (RNase/DNase free, Promega) and 1 μL of five-fold diluted DNA-

2.2. Experimental design and deadwood sampling The samples of downy birch lying deadwood were collected in 28 circular sampling plots (13 m radius; 531 m2) randomly located throughout the study area. In each sampling plot, lying deadwood volume was estimated through the Line Intersect Sampling (LIS) method (Warren and Olsen, 1964; Van Wagner, 1968) using two transects of 26 m perpendicularly located within the circular sampling plot in a North-East and SouthWest direction. In addition, deadwood samples were subjectively selected in the sampling plots in order to collect 4 samples within each of decay class using a five-class system (20 deadwood samples in total). The five decay classes were assigned by using a visual classification system considering the main visual characteristics of deadwood such as the presence of small branches, the softness of wood, the rot extension and the development of fungal mycelium (Næsset et al., 1999; Fridman et al., 2000; Paletto and Tosi, 2009; De Meo et al., 2017). The visual assessment of rates of decay was executed by two forest technicians, working together. The description of visual characteristics used to assign decay class of deadwood are reported in Table 1. The samples of lying deadwood were collected using a battery drill (20.4 V) with a modified bit following the procedure proposed by Paletto and Tosi (2009). They consisted in cylindrical cores of fixed diameter (3 cm), while the length was variable, and it was measured by a caliper with an accuracy of 1/10 mm. The samples were drowning from the middle of the log piece with the drill bit towards the ground. The collected cores were immediately placed in a sterile plastic bag and maintained at 4 °C for the period of the field survey (10 days). Within few days, samples were transported to laboratory and then store at −80 °C prior subsequent analysis. 2.3. DNA extraction

Table 1 Average values of moisture, total C and N contents, C/N ratio, and pH of the downy birch deadwood cores from the different decay classes. Moisture, total C and N contents were calculated as percentage of the total dry mass of deadwood, in relation to decay classes. Standard errors in parentheses. Different letters in a column indicate significant differences at P < 0.05 (LSD test) among means. Moisture (%) Class Class Class Class Class

1 2 3 4 5

22.7 39.1 50.5 57.3 63.2

(5.6) (5.9) (5.3) (6.9) (6.2)

Total N (%) c bc ab a a

0.28 0.41 0.42 0.53 0.70

(0.02) (0.06) (0.03) (0.05) (0.14)

Total C (%) c bc bc b a

44.8 45.5 45.0 45.4 45.0

3

(0.2) (0.9) (0.3) (0.8) (1.0)

C/N Ratio 159.2 117.6 109.5 88.9 73.1

(9.4) a (13.8) ab (7.8) ab (9.5) b (15.5) b

pH 5.2 4.4 5.1 4.7 5.5

(0.2) (0.2) (0.2) (0.2) (0.1)

ab c ab bc a

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Fig. 1. Soil (A) and deadwood (B) respiration measurements.

extracts or standard DNA. The primer pairs 357F-518R (Sánchez et al., 2007), EF390-FR1 (Vainio and Hantula, 2000), F243-R513 (Heuer et al., 1997), and 1106F-1378, (Watanabe et al., 2006) were used to determine the SSR rRNA gene copy number of bacteria, fungi, actinobacteria, and archaea, respectively. Negative control and standard curve were run in each plate. All samples and standards were run in triplicate. Data output were released by Opticon Monitor software (version 2.03 MJ Research). To check for product specificity and potential primer dimer formation, runs were completed with melting analysis starting from 60 °C to 95 °C with temperature increments of 0.5 °C and transition rate of 5 s. Optimization assay conditions were performed for each template DNA concentration and a linear regression of the threshold cycle (CT) for different DNA dilution vs the log dilution of pooled DNA was used to estimate PCR efficiency (E = 10−1/slope) for each primer pair (Pfaffl, 2001). For absolute quantification, standard curves were prepared using purified PCR products of known concentrations of the following fungal, bacterial, actinobacterial and archaeal pure cultures as template: Aureobasidium lividum (isolated from Silene paradoxa seeds), Enterobacter aerogenes ATCC13048, Streptomyces griseus DSM40236, and Methanococcus vannielii ATCC35089, respectively. Stock concentration (gene copies μL−1) was determined by Bio Photometer (Eppendorf Hamburg, Germany). Standard curves were freshly prepared with fivefold dilutions ranging from 5.5 108 to 8.8 105 SSR gene copies μl−1. The abundance of microorganisms in deadwood samples was expressed as SSR gene copy number in extracted DNA per gram of deadwood dry weight. Since the efficiency of each primer pair did not differ more than 10%, the relative abundance of fungi, actinobacteria and archaea vs bacteria in deadwood samples was calculated according to the formula R = 2−ΔΔC (Pfaffl, 2001). The number of fungal, actinobacterial, and T archaeal SSR gene copies in the different decay classes was normalized relative to the number of copies of SSR gene found in the first decay class. Decay classes with a 2−ΔΔC value below 1 have a lower SSR gene T copies than decay class 1, while decay classes with a 2−ΔΔC value above T 1 have a higher SSR gene copies than decay class 1.

of N and C was measured by dry combustion on a Thermo Flash 2000 NC soil analyzer (Fisher Scientific, Waltham, MA, USA) equipped with a thermal conductivity detector.

2.6. Physico-chemical properties

Data of deadwood physico-chemical properties, microbial diversity indices, and respiration measurements were analysed by one-way analysis of variance (ANOVA) followed by Fisher least-significant difference (LSD) post-hoc test to assess the significance of differences between mean values (P ≤ 0.05) by using Statistica 7 software (StatSoft, Palo Alto, CA, USA). The normality and the variance homogeneity of the data were tested prior to ANOVA. Pearson correlation analysis was performed between deadwood physico-chemical properties (total C, total N, C/N, moisture and pH) and total abundance of microbial groups by PAST3 software (Hammer et al., 2001). The banding patterns of each DGGE extracted as band-intensity

2.7. Soil and deadwood CO2 measurement The soil and deadwood respiration measurements were conducted in situ with an InfraRed Gas Analyser (IRGA) operating in closed-path mode. The environmental gas monitor (EGM-4, PP systems, UK) was equipped with the SRC-1 SR cuvette placed on top of soil or on lying deadwood. Each measurement was made to last twice the time taken to stabilize the rate CO2 efflux from soil and deadwood (usually within two min). A quadratic model was fitted to the measured data to obtain the rate of CO2 emission. At the time of each measurement, soil temperature was recorded using a manual digital thermometer at a depth of 3–5 cm. Soil respiration was measured on bare soil after removing the forest floor layer and vegetation (Fig. 1A). The data were collected in the 28 circular sampling plots where deadwood volume, biomass and C-stock were estimated. Overall, 88 sampling points have been measured and subsequently grouped by dividing sampling plots in three different groups considering the amount of deadwood volume (0–20, 20–40, > 40 m3). To analyze the response of soil CO2 emissions to soil temperature, the following first-order exponential function was used: SR = a ⋅ ekT where SR is the measured soil CO2 efflux, a is the value of SR at 0 °C (intercept), k is the rate of the SR increase (slope) and T is the soil temperature in °C. Q10 express the temperature sensitivity of soil respiration and it was calculated by inserting the parameter k into the formula: Q10 = exp10*k Deadwood respiration was measured planting the cuvette directly on downy birch samples, after removing bark and mosses (Fig. 1B). For analysis of deadwood respiration, data were averaged by decay class starting from decay class 2, while decay class 1 was not considered because too much hard wood consistence hampered cuvette insertion. 2.8. Statistical analyses

Moisture was determined by measuring fresh weight and dry weight after incubation at 50 °C for 48 h. After the first coarse ground described above, pH was determined by shaking 1 g of dried deadwood sawdust in 10 mL of distilled water for 120 min, let to decant for 30 min, and measuring the pH of the resulting aqueous extract using a digital calibrated pH meter (Hanna-pH211, Hanna Instruments, Padova, Italy). Successively, deadwood samples were homogenized with a cutting mill (Retsch SM 100), at rotor speed of 1500 rpm min−1, until a final fineness of 0.25 mm, and nitrogen and carbon contents were measured. To this aim, 10–20 mg samples were weighed into Ag-foil capsules and % 4

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matching tables were normalised by calculating the relative intensity of each band (ratio of the intensity of each band divided by the sum of the intensities of all bands in the same lane) and imported into PAST3 for multivariate statistical analysis. Non-metric multidimensional scaling (nMDS) was used to visualize differences of each DGGE profile in a twodimensional space. One-way analysis of similarity (ANOSIM) and permutational analysis of variance (PERMANOVA) followed by pairwise comparisons were conducted to determine the extent of differences in microbial communities among decay classes. nMDS, ANOSIM, and PERMANOVA were performed using the Bray-Curtis distance measure and 9999 permutational tests; the accuracy of the nMDS plots was determined by calculating a 2D stress value. Finally, binary matrices of fungal, bacterial and archaeal PCRDGGEs were related to deadwood physico-chemical properties by canonical correspondence analysis (CCA) performed with PAST3, in order to find potential connection between changes in physico-chemical and microbiological deadwood composition due to decay status. DGGE bands were used as “OTU” data (filled symbols), while deadwood physico-chemical properties as “environmental” variables (vectors); the statistical significance was assessed using 999 permutational tests. The distances between the symbols reflect their dissimilarity; a symbol’s position in relation to a vector head is a function of the correlation between the chemical parameter of deadwood sample and the microbial species. The length of vector reflects the relative importance of the chemical parameter in discriminating the microbial community of each decay class (Zhang et al., 2008).

found in decay class 5 in bacterial community and in decay class 3 in both fungal and archaeal communities. These indices calculated on actinobacterial 16S-DGGE profiles showed a different trend and the highest values were found in samples of decay class 2 and the lowest values were found in samples of decay class 5. In all nMDS ordinations, the replicates of decay class 1 were grouped on left side, distinctly separated from the others ribotype profiles. The replicates of decay classes 3, 4, and 5, were found on right side of the graphical representation and exhibited a slight overlapping in which no distinct grouping could be highlighted (Fig. 2). The replicates of decay class 2 were positioned in an intermediate position in relation to class 1 and classes 3–5 replicates. The nMDS graphical representations might reflect important modification of the structure of microbial community of deadwood from early (decay class 1), middle (decay class 2) and middle and late (decay classes 3–5) stages of decomposition. Except for the archaea, the relative positions of the points in the nMDS were weakly reliable and DGGE profiles were further analysed by multivariate analysis. The outcomes of the ANOSIM global test (Table 3) confirmed the nMDS graphical representations, since R values > 0.5 were interpreted as separated but overlapping (Ramette 2007). However, this analysis showed that decay class significantly affected the community structural diversity of all microbial groups (P < 0.001). Generally, in all microbial groups the ANOSIM pairwise comparisons between decay classes (Table 4), showed the R values greatest separation (R > 0.75, P < 0.05) occurred between decay class 1 and decay classes 4–5 followed by decay class 2 and class 5. The lowest separation was found in actinobacterial community between decay class 1 and class 2 (R = 0.135, P = 0.2). Same significant results were obtained by PERMANOVA analysis (Tables 3 and 4).

3. Results 3.1. Physico-chemical parameters analysis The results of determination of moisture, total C and N contents, and pH conducted on the downy birch deadwood samples are reported in Table 1. Data were averaged according to the different decay classes. Both moisture and total N values significantly increased with increasing decay class. Moisture increased with a logarithmic trend (R2 = 1) and ranged from 22.7% of decay class 1 to 63.2% of decay class 5. Total N content increased with an exponential trend (R2 = 0.95) and ranged from 0.28% of decay class 1 to 0.70% of decay class 5. Samples of middle decay classes (from 2 to 4) did not show significant differences in averaged N content whereas samples of the early decay class 1 and late decay class 5 showed the significant lowest and highest values, respectively. The percentage of C showed a linear trend and all averaged values did not deviate a lot from 45%. As a consequence, the C/N ratio significantly decreased from the early decay class 1 (159.2) to the late decay classes 4 and 5 (88.9 and 73.1, respectively) through intermediate averaged values of decay classes 2 and 3 (117.6 and 109.5, respectively). The pH values were settled at about 5 with the significant lowest value in decay class 2 (4.4) and the significant highest value in decay class 5 (5.5).

3.3. Real time PCR analysis Using absolute quantification, the abundance of each microbial group increased as the decomposition progresses. Bacteria resulted the significant most abundant microbial group in each decay class. Bacteria were observed to be present at a consistent level ranging between 6.56 109 and 6.84 1010 16S rDNA copies per gram of soil (Table 5) from decay class 1 to class 5. A similar trend was observed for archaea and actinobacteria although the latter were observed nearly 10-fold lower than bacteria and archaea (Table 5). Fungi reached a maximum of 2.17 108 18S rDNA copies per gram of soil in decay class 4 and were observed as the lowest abundant microbial group in each decay class. The monitoring of population changes during decomposition was also carried out using the relative quantification assay. To this aim, the 16S rRNA gene amplified with total bacteria primers pair was used as a “housekeeping gene” for normalization of the data by ΔΔCT method. The abundance of fungi, actinobacteria, and archaea per decay class was calculated relatively to the abundance of bacteria of decay class 1 (Fig. 3). No significant differences were found regarding actinobacteria vs bacteria (Fig. 3B) which ratio remaining nearly similar as decomposition progresses. Fungi of decay class 2 were observed relatively 0.5fold higher than fungi of decay class 1, whereas they tend to significantly decrease as decomposition progresses (Fig. 3A). Archaea from decay classes 2–5 were observed nearly 0.5-fold significantly lower than archaea of decay class 1 (Fig. 3C).

3.2. PCR-DGGE analysis The analysis of the deadwood microbial communities based on the DGGE profiles (Fig. S1) showed that the diversity and community structure were affected by deadwood decomposition as the number of bands and their position differed in each decay class. Overall, bacteria showed the highest values of both richness and Shannon index (ranged from 20.0 ± 1.1 to 26.0 ± 1.5 and from 2.96 ± 0.06 to 3.23 ± 0.06, respectively) while actinobacteria showed the lowest values (ranged from 4.3 ± 0.5 to 11.3 ± 0.8 and from 1.42 ± 0.12 to 2.39 ± 0.07, respectively). A comparison of these indices according to the different decay classes, showed significant differences in each microbial community analyzed (Table 2). In particular, decay class 1 showed the lowest values of both indices in bacterial, fungal and archaeal communities. The highest values were

3.4. Correlation of physico-chemical and microbiological data Significant Pearson’s correlation coefficients between physico-chemical parameters of deadwood samples and absolute abundances of fungi, bacteria, actinobacteria and archaea inhabiting deadwood are reported in Table 6. Total N and C/N ratio resulted to be positively correlated to all the four microbial groups. Conversely, total C did not show any significant correlation. Bacterial abundance resulted positively correlated to moisture whereas fungal abundance resulted 5

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Table 2 Average species richness and Shannon diversity index calculated on 18S rDNA-DGGE band profiles from fungal community and on 16S rDNA-DGGE band profiles from bacterial, actinobacterial and archaeal communities in decaying downy birch deadwood, on the basis of decay class; standard error in brackets. Different letters in a column indicate significant differences at P ≤ 0.05 (LSD test) among means. Fungi (18S-DGGE) Richness Class Class Class Class Class

1 2 3 4 5

9.0 10.3 14.5 13.3 13.0

(0.9) (0.9) (3.3) (1.8) (0.8)

Shannon b ab a ab ab

2.15 2.26 2.58 2.53 2.52

(0.10) (0.09) (0.23) (0.13) (0.06)

b ab a ab ab

Bacteria (16S-DGGE)

Actinobacteria (16S-DGGE)

Archaea (16S-DGGE)

Richness

Richness

Richness

20.0 21.5 22.2 23.8 26.0

Shannon

(1.1) (0.3) (2.3) (1.9) (1.5)

b ab ab ab a

2.96 3.01 3.06 3.12 3.23

(0.06) (0.01) (0.10) (0.07) (0.06)

b b ab ab a

8.0 11.3 11.0 10.0 4.3

Shannon (2.1) (0.8) (1.8) (1.9) (0.5)

ab a a a b

1.93 2.39 2.34 2.24 1.42

(0.31) (0.07) (0.17) (0.19) (0.12)

ab a a a b

12.8 17.8 20.3 17.5 16.8

(1.7) (0.5) (1.8) (1.7) (0.6)

Shannon b ab a ab ab

2.49 2.84 2.96 2.81 2.77

(0.13) (0.02) (0.09) (0.10) (0.03)

b ab a ab ab

Fig. 2. nMDS ordination plots of fungal 18S rDNA (A, stress = 0.340); bacterial 16S rDNA (B, stress = 0.279); actinobacterial 16S rDNA (C, stress = 0.290); and archaeal 16S rDNA (D, stress = 0.216) obtained from downy birch deadwood.

1 resulted to be positively correlated to C/N ratio whereas the ones of late class 5 resulted positively correlated to pH values. The total C and N content showed to be positively correlated to microbial community of the intermediate decay class 3 and 4 and negatively with those of decay class 2.

Table 3 ANOSIM and PERMANOVA global test based on the Bray-Curtis similarity matrices of 18S- and 16S-DGGE, for microbial groups (fungi, bacteria, actinobacteria and archaea) decaying downy birch deadwood. ANOSIM

Fungi (18S-DGGE) Bacteria (16S-DGGE) Actinobacteria (16S-DGGE) Archaea (16S-DGGE)

PERMANOVA

R

P

F

P

0.56 0.54 0.53 0.60

0.0001 0.0001 0.0001 0.0001

2.86 2.31 2.88 3.89

0.0001 0.0001 0.0001 0.0001

3.5. Soil and deadwood respiration Soil respiration showed a significant relationship (p < 0.001) with temperature, following an exponential function and a Q10 = 4.04 (Fig. 5). When grouped considering different deadwood amount (Fig. 6A), according to Paletto et al. (2019), sampling plots containing 0–20 m3 and 20–40 m3 of deadwood volume showed similar values of soil CO2 fluxes (about 0.65 g CO2 m−2h−1). In sampling plots with more than 40 m3 of deadwood volume soil CO2 fluxes clearly increased by about one-third, although not significantly (P ≤ 0.05).

negatively correlated to pH. Moreover, chemical parameters were also correlated to microbial community structures of fungi, bacteria and archaea by CCA analysis (Fig. 4). The deadwood microbial communities of the early decay class 6

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Table 4 Values of ANOSIM statistic R (upper right side) and PERMANOVA statistic F (lower left side) from pairwise comparison of the banding profiles of fungal 18S-DGGE and bacterial, actinobacterial and archaeal 16S-DGGEs. Fungi Class Class Class Class Class

Class 1 1 2 3 4 5

2.45* 3.63* 4.39* 4.62*

Bacteria Class 1 Class 2 Class 3 Class 4 Class 5

2.32* 2.15 3.28* 3.60*

Actinobacteria Class 1 Class 2 Class 3 Class 4 Class 5

2.40* 2.77* 3.44* 6.13*

Archaea Class 1 Class 2 Class 3 Class 4 Class 5

2.72* 6.08* 6.64* 6.81*

Class 2

Class 3

Class 4

Class 5

0.38

0.73* 0.46*

0.91* 0.40* 0.0833

0.86* 0.88* 0.31 0.53*

2.17* 2.04* 4.33*

1.38 1.96

0.65*

0.56 0.43

1.89 1.83 3.07*

0.95 2.35*

0.43*

0.52* 0.14

1.38 2.54* 5.07*

1.01 2.50

0.39*

0.92* 0.55*

2.81* 2.47* 4.07*

2.46 2.18*

2.68* 0.93* 0.40 0.01 2.03* 0.68* 0.47* 0 3.03* 0.93* 0.40* 0.45 2.49*

0.88* 0.74* 0.55* 0.24*

0.93* 0.96* 0.43 0.53*

0.99* 0.70* 0.56* 0.44*

* Significant value (P < 0.05).

Deadwood respiration data are shown in Fig. 6B averaged by decay classes starting from decay class 2. Deadwood CO2 fluxes significantly increased with an exponential trend (R2 = 0.99) ranged from 0.06 g CO2 m−2h−1 of the decay class 2 to 2.37 g CO2 m−2h−1 of the late decay class 5. 4. Discussion Our interest in deadwood concerns mainly aspects related to microbial diversity and atmospheric carbon cycle. Regards the first aspect, the diversity indices and absolute abundance of wood inhabiting microbial communities tend to increase over the deadwood decomposition. This statement is well-founded for total bacteria that showed increasing values of richness, Shannon index and abundance of 16S rRNA gene copies, from decay class 1 to decay class 5. For the other microbial groups, the increasing trend was not always linear. In general, the lowest values of both diversity indices and absolute abundance were found in decay class 1. Since initial colonization of wood by decomposing organisms is considered to some extent a stochastic event (Dickie et al., 2012; Magnússon et al., 2016) we may assume that a few number of the microbial taxa that reach the wood in the early stage may possess the ability to decompose most of the plant polymers. In fact, deadwood is a complex substrate composed of a heterogeneous assemblage of relatively easily degradable compounds,

Fig. 3. Relative abundance calculated using the 2−ΔΔC method (Pfaffl, 2001) of T fungi (A), actinobacteria (B), and archaea (C) vs bacteria in downy birch deadwood samples according to decay class. One-way ANOVA followed by LSD test at P ≤ 0.05 was performed; different letters above bars indicate significant differences in relative abundance among decay classes.

such as cellulose, combined with several types of complex biopolymers, such as lignin, creating a very recalcitrant nutrient resource, difficult to access and utilize for most organisms (Pan et al., 2011; Hoppe et al., 2016; Purahong et al., 2018a). As the decomposition progresses,

Table 5 Real time PCR analysis from the DNA fraction of the decay classes 1–5. Values are means with standard error in brackets. Different letters in a column indicate significant differences at P ≤ 0.05 (LSD test). Target gene 18S rRNA (Fungi) Class Class Class Class Class

1 2 3 4 5

6.00 1.75 7.36 2.17 1.14

7

10 108 107 108 108

16S rRNA (Bacteria) (2.4 (5.5 (3.2 (4.6 (4.9

7

10 ) 107) 107) 107) 107)

b ab b a ab

6.56 8.03 1.28 4.62 6.84

9

10 109 1010 1010 1010

(3.9 (4.2 (2.3 (1.6 (2.9

16S rRNA (Actinobacteria) 9

10 ) b 109) b 109) b 1010) ab 1010) a

7

5.92 6.82 1.39 4.84 9.64

8

10 108 109 109 109

(3.7 (4.0 (3.9 (1.8 (4.4

16S rRNA (Archaea) 8

10 ) 108) 108) 109) 109)

b b b ab a

3.28 2.20 3.50 1.09 1.85

109 109 109 1010 1010

(1.5 (1.5 (5.1 (3.9 (7.7

109) 109) 108) 109) 109)

b b b ab a

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Table 6 Pearson’s correlation matrix of chemical parameters and the four microbial groups abundance measured in deadwood samples from the different decay classes. Only significant Pearson's correlation coefficients (r-values) are reported. Fungi Total N Total C C/N Moisture pH

**

0.45 n.s. −0.48** n.s. −0.56***

Bacteria ***

0.86 n.s. −0.72*** 0.39* n.s.

Actinobacteria ***

0.85 n.s. −0.69*** n.s. n.s.

Archaea 0.86*** n.s. −0.70*** n.s. n.s.

n.s. = not significant. * P < 0.1. ** P < 0.05. *** P < 0.01.

Fig. 5. Exponential function of soil respiration with temperature. Function parameters, R2 and Q10 values are reported.

degradative activities of the first colonizers render deadwood more accessible to other organisms. Thus, the number of microbial taxa able to utilize the transformed substrates may increase and, as consequence, increase also their total abundance. Microorganisms inhabiting the early stage of deadwood decomposition may possess peculiar degradative ability, successively trophic interaction among colonizing microorganisms and competition contribute to a succession of woodinhabiting microbial taxa with different metabolic ability (Purahong et al., 2016a, 2018a; Magnússon et al., 2016; Pastorelli et al., 2017). Each MDS ordination diagram supported these statements, showing the decay classes 1 and 2 clearly separated from the other classes. Conversely, the DGGE profiles of decay classes 3, 4 and 5 were quite overlapping, suggesting a significant variability of microbial community composition in middle and late stage of wood decomposition. Bacteria dominated the microbial communities in our deadwood samples (Kielak et al., 2016). Bacteria have a limited activity to decompose polymeric ligninocelluloses but they possess a great metabolic versatility and redundancy (Cornelissen et al., 2012; Purahong et al., 2016a). In agreement with the fact that their absolute abundance and the diversity indices increase with the progress of wood decomposition,

our results let us to suppose that the heterogeneity of niches available during the late stages of wood decomposition support a highly diverse bacterial community (Schneider et al., 2012; Bani et al., 2018; Pastorelli et al., 2017). As above, nMDS evidenced that each deadwood decay class harboured a quite distinct bacterial community highlighting a successional pathway of different bacterial species over the deadwood decomposition. Fungi showed the highest values of both richness and Shannon index in decay class 3 in agreement with Bani et al., (2018) that found highest diversity in the intermediate decay stages. In contrast, decay class 3 showed the lowest abundance of 18S rRNA gene copies. Fungi may secrete oxidoreductases and hydrolases that mineralize or decompose most plant cell wall polymers into simple compounds (Floudas et al., 2012; Purahong et al., 2016a, 2018a) modulating the availability of resources for themselves and also for other microbial groups (Mäkipää et al., 2018). Like bacteria, fungal communities undergo to a clear succession of taxa able to decompose the available biopolymers. Since Ascomycota have been shown to possess a limited ability to degrade lignin-like compounds and are mainly involved in cellulose Fig. 4. CCA ordination plot (trace 0.81; P < 0.01) of the fungal, bacterial and archaeal communities and deadwood chemical variables defined by the first and second axis, accounting the 35.7% (P < 0.01) and 26.2 (P < 0.01) of total variability, respectively. To identify taxa that mainly contributes to the separation of the microbial communities of the different deadwood samples, DGGE band scores were plotted on the ordination diagram (red dots, fungal DGGE bands; light blue dots, bacterial DGGE bands; green dots, archaeal DGGE bands). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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stages. It is known that actinobacteria possess good capabilities in degrading insoluble organic polymers, such as cellulose, and presumably they can degrade lignin-like compounds presumably more abundant in the intermediate wood decomposition processes (De Boer and Van der Wal, 2008; Pastorelli et al., 2017). Nevertheless, their contribution to wood decay is still unclear. The relative abundance of actinobacteria group showed no significant differences on the basis of decay classes, suggesting that the proportion of the number of actinobacteria respect total bacteria does not change over the deadwood decomposition. Regarding archaeal group, their number tend to increase over the deadwood decomposition. Conversely, the richness and Shannon index showed the highest values in the decay class 3. As for fungi this class may represent a passing stage in which the dominating taxa involved in first phase of wood decomposition give the way to dominating taxa with different degradative ability. Respect to bacteria, their relative abundance significantly decreases from decay class 1 to the successive decay classes 2–5, suggesting a more important role of archaea in the early stages of decomposition respect to bacteria. Less is known about the catabolic activities of archaea in deadwood (Johnston et al., 2016). So far, cellulase and xylanase enzymes have been discovered in extremophilic Archaea (Wainø and Ingvorsen, 2003; Graham et al., 2011), but it is not known whether these enzymes can also be found in temperate Archaea (Rinta-Kanto et al., 2016). Archaea may be involved in CH4 emissions from deadwood as suggested by several authors (Covey et al., 2016; Gómez-Brandón et al., 2017; Pastorelli et al., 2017). Nonstructural labile carbon substrates stored in the early stages of wood decomposition may be important substrates for methanogenesis (Covey et al., 2016). Thus, lying deadwood may constitute an important source for this GHG. However, also non-methanogenic archaea have been found in decaying wood providing indications that together with bacteria, archaea are an integral and dynamic component of decaying wood biota (Rinta-Kanto et al., 2016). To date, Thaumarchaeta were found as prominent members of the archaeal populations in deadwood highlighting the versatility and cosmopolitan nature of this phylum in natural environment (Rinta-Kanto et al., 2016). As woody material decays, its physico-chemical properties gradually change. Our results indicate that the percentage of total C of downy birch deadwood is not influenced by the decay class remaining quite stable on the 45% over the advancement of wood decay. Decomposer organisms partition C between respiration and uptake into biomass. Starting from wood complex biopolymers, microorganisms are able to generate specific compounds (i.e. chitin, beta-glucan, melanin, glomalin, bacterans, glucosamine, mumaric acid) that were incorporated into microbial biomass contributing significantly to the C-stable pools (Kögel-Knabner, 2002; Lorenz and Lal, 2005). Conversely, the percentage of total N increases as wood decomposition progresses, with a slight and not significant increment in the early and middle decay classes (from 1 to 4) and a strong increase in the late decay class 5. Consistently, similar trends were observed in other tree species of both conifer and broadleaved (Allen et al., 2000; Olajuyigbe et al., 2011; Lombardi et al., 2008; Strukelj et al., 2013; Petrillo et al., 2016; Pastorelli et al., 2017). The increment of N along decomposition may be a consequence of different N inputs due to fungal translocation and/or N-fixation by bacteria (Gómez-Brandón et al., 2017; Bani et al., 2018). N is one of the most important drivers for changes in abundance and diversity of microbial communities in many environments. For example, the fungal wood degradation enzymes require high amount of N (Strukelj et al., 2013; Baldrian et al., 2016; Purahong et al., 2018b). In our samples, the increment of N is accompanied by an increment in microbial abundances, in particular bacteria, actinobacteria and archaea, as reflected by the positive correlation between total N percentage and microbial abundance calculated as number of copies of the small subunit rRNA gene via real time PCR. Thus, as decomposition progresses, the increment in N availability stimulates microbial growth. Moreover, CCA diagram displayed that increment in N content in the most advanced decay classes was associated

Fig. 6. Soil respiration (A) and deadwood respiration (B) expressed as g of CO2 emitted for m−2h−1 from areas with different deadwood volume and from deadwood of different decay classes, respectively. One-way ANOVA followed by LSD test at P ≤ 0.05 was performed; different letters above bars indicate significant differences in relative abundance among decay classes.

decomposition, they are considered as first colonizers of deadwood and litter (Schneider et al., 2012; Bani et al., 2018). Conversely, Basidiomycota possess a greater ability in degradation of recalcitrant lignincontaining material and appear later in the decomposition process (Schneider et al., 2012; Bani et al., 2018). Our results suggest that the decay class 3 may represent a passing stage in which the dominating taxa involved in first phase of cell-wall decomposition give the way to dominating taxa with stronger ligninolytic activity. Moreover, the relative abundance diagram showed that fungi are relatively more abundant in the early stages of decomposition reaching significant highest values in the decay class 2. Successively, bacteria overcome in number fungi in the last stages of decomposition (decay classes 4 and 5). Thanks to their decay activities fungi of the early stages weaken lignin barriers providing opportunities for bacterial access. Moreover, fungi break down complex molecules into suitable substrates providing opportunities for bacterial growth (Valášková et al., 2009; Pastorelli et al., 2017). Thus, late stages of decomposition are characterized by a decline of fungal activity and bacteria become more abundant and specialized in degradation of lignin derivates (Johnston et al., 2016; Kielak et al., 2016). Focusing the attention on the bacterial subgroup of the actinobacteria, we observed the highest values of both diversity indices in the intermediate decay classes, followed by a strong decrease in the late decay class 5. As fungi, also actinobacteria showed to play a more important role in the early, and in particular, in the intermediate stages of decomposition, suggesting a greater involvement in degradation processes of wood structural compounds such as cellulose and lignin. Successively, the environmental conditions and/or the substrates made available in the late phases of decomposition, contribute to create a more selective habitat for this bacterial group; as the decay progresses the actinobacterial abundance increases (as resulted by real time PCR) but a lower number of actinobacterial taxa are involved in the late 9

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with a greater number of microbial OTU. N is the most important nutrient limiting the ecosystem processes thus, its higher values in advanced decay stages indicated that decayed wood debris may be an important source of N over a long-time scale in terrestrial ecosystems (Lombardi et al., 2008). In the studied area of Khibiny Mountains, lying deadwood contributes to 70.7% of total deadwood volume thus playing an important role in forests ecosystems functioning (Paletto et al., 2019). The high lying deadwood volume accounted in the Khibiny Mountains (16.9 m3 ha−1) is due to the frequent occurrence of snowfall crashes, while the deadwood produced by human activities (silvicultural treatments) is very limited. As total N percentage increases, the C/N ratio significantly declines along the decay classes. The C/N ratio is often used to describe degradability of organic matter and a lower C/N ratio facilitates higher rate of decomposition (Błońska et al., 2017). Decaying wood is considered to have a high C/N ratio and thus a difficult substrate to degrade in early stage under N limitation. As decomposition progresses the C/N ratio decreases supporting the assumption that as decomposition progress the microbial communities more easily utilize organic compounds for their growth and activities (Purahong et al., 2018c). These broadly agree with the significantly negative correlation found between C/N and microbial abundance calculated via real time PCR. Consistent considerations were also highlighted by CCA, that showed C/N ratio negatively correlated with microbial assemblages in deadwood of the advanced decay classes. However, C/N ratio in the late decomposition stages still remains very high compared to other substrates such as soil or leaf litter (Purahong et al., 2018c). The trend of C/N ratio and microbial abundance along decomposition stage is in line with an increase of deadwood respiration, confirming a more easily access to organic compounds. Our in-situ measurements of CO2 emission from deadwood highlighted that, over the deadwood decay, the increase in microbial abundance corresponds to an increase of deadwood respiration. Indeed, deadwood enhanced the C accumulation in the soil environment with a simultaneous increase in the biological activity of the soil (Błońska et al., 2017). As a consequence, we found that soil respiration was higher in area containing greater amount of deadwood, thus highlighting potential positive feedback for climate change. Under current trends of climate warming (IPCC, 2007), an increase of CO2 emissions is expected, favored by higher temperatures. Respiration is a temperature dependent process and several studies provided evidences of increased soil respiration in response of small changes in temperature (Davidson & Janssens, 2006). The Q10 of 4 found in this study for soil respiration is in line with these expectations, showing potentially huge CO2 emissions from soil, for even small temperature increase. The boreal forests are considered to be an overall C sink and many authors have claimed that they sequester more than they emit (Sandström et al., 2007). A temperature increase and the response of soil respiration may reduce the sink capacity of these ecosystems, or even transforming them into CO2 sources. Deadwood pH is the only wood property which does not correspond to a precise trend with the advance of decomposition. However, pH significantly correlates with fungal abundance. Consistently, Purahong et al. (2018b) found that pH is the main factor influencing the woodinhabiting fungal community composition. Effects of pH on mycelial growth, decomposition ability of plant material and enzyme production by fungi have been found consistently across different habitats including soil, leaf, litter and deadwood (Kok et al., 1992; Schneider et al., 2012; Purahong et al., 2016b, 2018b). Specific fungal communities and taxa can change wood pH to levels suitable for their growth and survival (Humar et al., 2001). Ammonia released from decomposition processes operated by macrofungal fruitbodies and from excretion of invertebrate, may causes an increase of pH (Ingelög and Nohrstedt, 1993; Purahong et al., 2018b) as well as bacteria and archaea through ammonification, N-fixation and denitrification (Stein and Klotz, 2016). N-fixing bacteria and known bacteria involved in

dissimilatory nitrite reduction to ammonium (e.g., Clostridium, Klebsiella) have been detected in both conifer and broadleaved deadwood logs (Hoppe et al., 2014, 2015; Johnston et al., 2016; Mäkipää et al., 2018; Probst et al., 2018). Finally, CCA reveled also that as decay progresses, the fungal, bacterial and archaeal assemblages resulted influenced by moisture. Moisture is considered among the most important driving forces affecting microbial abundance and activity in natural environment such as deadwood and soil (Jomura et al., 2008; Manzoni et al., 2012; Gómez-Brandón et al., 2017). It increases with increasing wood decay stages due to the tendency of deadwood of advanced decay classes, to reabsorb atmospheric humidity (Paletto and Tosi, 2009; Pichler et al., 2012). All things considering, the changes in wood physico-chemical properties occurring during decay are a direct consequence of by-products arise from decomposition processes and, at the same time, influence the metabolic activity of wood-inhabiting microbial species by selecting the most suitable to the new environment (Mäkipää et al., 2018). 5. Conclusions To the best of our knowledge, this is the first study aiming to evaluate the microbial community composition and abundance of four microbial groups (fungi, bacteria, actinobacteria and archaea) over decomposition process in downy birch lying deadwood under boreal climate. Moreover, we try to add new information on the potential involvement of deadwood in climate change by assessing in situ-measuring CO2 emissions. This field experiment is among the first attempts to investigate the effect of the presence of deadwood on CO2 efflux in a natural boreal forest. Overall, our results highlighted a multiple role of deadwood in boreal ecosystems, i) a suitable habitat for many microorganisms in which the succession of microbial taxa is a direct consequence of modifications in wood chemical composition, ii) a large and persistent C stock, iii) a N source for microorganisms, iv) a transient pool in part transferring C to soil organic matter and in part emitting C as CO2 in the atmosphere. This last mechanism has positive and negative implications for climate change, considering either long-term C storage in soil or short-term CO2 emissions from deadwood and soil. Thus, it is important to quantify CO2 efflux from boreal forest ecosystems and to understand how it is affected by forest management and climate change. In particular, forest management practices and extreme events due to climate change can strongly influence the lying deadwood distribution by decay class. In the case of an abnormal distribution of the lying deadwood volume – concentrated in one or two decay classes – it would be appropriate to rebalance the deadwood distribution in the various classes. In this way the situation will be as close as possible to a boreal forest characterized by natural evolution. Therefore, the forest management practices suitable in this kind of forest ecosystem should be based on the close-to-nature principles. Acknowledgments The authors want to thank the researchers of Khibiny Educational and Scientific Station of the Faculty of Geography M.V. Lomonosov Moscow State University Government with special regard to Dr. Yulia Zaika. This work was funded by the European Union’s Horizon 2020 project INTERACT. References Allen, R.B., Buchanan, P.K., Clinton, P.W., Cone, A.J., 2000. Composition and diversity of fungi on decaying logs in a New Zealand temperate beech (Nothofagus) forest. Can. J. For. Res. 30, 1025–1033. https://doi.org/10.1139/x00-037. Baldrian, P., Zrůstová, P., Tláskal, V., Davidová, A., Merhautová, V., 2016. Fungi

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