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Plant and Soil

, Volume 371, Issue 1–2, pp 139–154 | Cite as

A closeup study of early beech litter decomposition: potential drivers and microbial interactions on a changing substrate

  • Christian Brandstätter
  • Katharina Keiblinger
  • Wolfgang Wanek
  • Sophie Zechmeister-Boltenstern
Open Access
Regular Article

Abstract

Aims

Litter decomposition and subsequent nutrient release play a major role in forest carbon and nutrient cycling. To elucidate how soluble or bulk nutrient ratios affect the decomposition process of beech (Fagus sylvatica L.) litter, we conducted a microcosm experiment over an 8 week period. Specifically, we investigated leaf-litter from four Austrian forested sites, which varied in elemental composition (C:N:P ratio). Our aim was to gain a mechanistic understanding of early decomposition processes and to determine microbial community changes.

Methods

We measured initial litter chemistry, microbial activity in terms of respiration (CO2), litter mass loss, microbial biomass C and N (Cmic and Nmic), non purgeable organic carbon (NPOC), total dissolved nitrogen (TDN), NH4 +, NO3 - and microbial community composition (phospholipid fatty acids – PLFAs).

Results

At the beginning of the experiment microbial biomass increased and pools of inorganic nitrogen (N) decreased, followed by an increase in fungal PLFAs. Sites higher in NPOC:TDN (C:N of non purgeable organic C and total dissolved N), K and Mn showed higher respiration.

Conclusions

The C:N ratio of the dissolved pool, rather than the quantity of N, was the major driver of decomposition rates. We saw dynamic changes in the microbial community from the beginning through the termination of the experiment.

Keywords

Leaf litter decomposition Microbial biomass Microcosm Microbial community structure analysis Microbial respiration 

Abbreviations

AK

Achenkirch

C

Carbon

Ca

Calcium

Cmic

Microbial biomass carbon

Cmic:Nmic

C:N microbial biomass

DOC

Dissolved organic carbon

dw

Dry weight

ECD

63Ni-electron-capture detector

FB-ratio

Fungal-bacterial ratio

Fe

Ferrum

FID

Flame ionisation detector

K

Potassium

K

Decomposition constant

KL

Klausen-Leopoldsdorf

Mg

Magnesium

ML

Litter mass loss

N

Nitrogen

Nmic

Microbial biomass nitrogen

NPOC

Non purgeable organic carbon

OR

Ort

P

Phosphorus

PE

Perg

PC

Principal component

PCA

Principal component analysis

PLFA

Phospholipid fatty acid

qCO2

Microbial metabolic quotient

NPOC:TDN

C:N ratio of non purgeable organic C and total dissolved N

TDN

Total dissolved nitrogen

TIN

Total inorganic nitrogen

Zn

Zinc

Introduction

Climate and substrate quality are the main drivers of leaf litter decomposition rates (Meentemeyer 1978; Prescott 1995). Specifically, the chemical composition of litter (e.g. lignin- and N-content) has been shown to strongly affect decomposition rates (Melillo et al. 1982; Prescott 2010). Usually, to assess litter decomposition rates two basic methods are used: the litter bag-technique or microcosm studies (Salamanca et al. 1998). In the present study, we controlled variation of climatic factors to a minimum by using the microcosm approach (Taylor et al. 1989), which allowed us to more exclusively focus on the impact of substrate quality and initial nutrient stoichiometry on decomposition. Even though increasing numbers of studies have dealt with decomposition rates in early stages (Prescott 2010), we propose that the importance of the dynamics of the early stages of decomposition has been widely overlooked. In the present study we more explicitly explore the time course of very early litter decomposition. The chosen high temporal resolution allowed us to find new insight into the very early microbial and nutrient dynamics in a leaf litter system.

Initial litter composition is thought to control litter decomposition rates (Jacob et al. 2010) as well as initial nutrient content and relative amounts of labile and refractory carbon (C) (Aber and Melillo 1991; Melillo et al. 1982). During the early phase, C is relatively available and nutrients are often a limiting factor, since immobilization of the limiting nutrient (usually N) occurs regularly (Prescott 2005). Litter with greater initial N content has been shown to decompose at a faster rate because it poses a smaller discrepancy in the C:N ratio between litter and decomposer microbes, and so microbes will need to immobilize less N to decompose the litter (Aber and Melillo 1991).

During litter decomposition the C pool is subject to qualitative changes: easily accessible substances like cellulose and C-containing cell solubles are preferably decomposed, which leads to an increase in more recalcitrant C-containing molecules like lignin and lignified cellulose and a slowing of the decomposition rate (Berg 2000; Berg et al. 1993). The impact of N on decomposition is more controversial. So far, the classification of the time into “short-term” and “long-term” litter decomposition is relatively common (Berg and McClaugherty 2008). Nitrogen (N) seems to accelerate decomposition in earlier stages due to a higher microbial nutrient demand (Moorhead and Sinsabaugh 2006), whereas in later stages it is thought to decelerate litter decomposition (Gallo et al. 2004). This issue has been addressed as “microbial mining theory” (Craine et al. 2007). In a recalcitrant substrate, certain guilds of microbes have to invest more energy and therefore be able to access nutrients that are closely tied to the substrate. If enough N is available, there is no need to decompose recalcitrant substances to access nutrients, thus decomposition slows down despite high N-availability.

Other possible mechanisms for the inhibition of late stage decomposition by N additions include a proposed negative effect of N on C mineralization, or a negative effect on lignin degradation by inhibiting lignin degrading enzymes, resulting in reduced availability of the structural carbohydrates in the lignocellulose complex (Chesson et al. 1997; Henriksen and Breland 1999).

In addition to the abundant C and N, other elements have been discussed to be able to control litter decomposition, mainly Mn and P. In general, litter poor in P is suggested to have higher F/B ratios (Hieber and Gessner 2002; van der Wal et al. 2006; Güsewell and Gessner 2009). A previous study at these same sites as the current study has noted a strong impact of P on the microbial community composition (Schneider et al. 2012) and another one on the early phases of litter decomposition at other sites (Aerts and de Caluwe 1997). Similarly, Mn has been mentioned in relation to fungal decomposition, as fungi use Mn-peroxidase-enzymes for lignin degradation (Hofrichter 2002). Fungi have the ability to degrade highly recalcitrant compounds like lignin by excreting extracellular enzymes (Boer et al. 2006). Although with PLFA-analysis it is not possible to distinguish between different fungal phyla, predominant fungal phyla in the soil-litter interface (Osono and Takeda 2006), are considered to be Ascomycota and Basidiomycota. While Ascomycota have been mentioned as first colonizers during beech litter decomposition at similar Austrian sites (Schneider et al. 2012), they are regarded primarily as cellulose decomposers or sugar fungi (Osono 2007) and their ability to degrade lignin is limited. On the other hand, the succession at later stages of decomposition tends towards Basidiomycota at these same Austrian sites (Schneider et al. 2012), which have previously been shown to be controlled by the Mn content of the original litter (Berg et al. 2007). Within the decomposer community fungi and bacteria exhibit different nutrient demands and constraints on the decomposition processes, (Keiblinger et al. 2012), and thereby influence decomposition including the C use efficiency (Sinsabaugh et al. 2009; Keiblinger et al. 2010).

In the current study we asked: (i) Would higher initial N content and lower C:N ratios accelerate early stage decomposition rates (Schneider et al. 2012; Zhang et al. 2008) of lignin-rich (Melillo et al. 1982) and thus recalcitrant beech litter (Mungai and Motavalli 2006)? (ii) Which other elements in addition to, or beside, N possibly influenced the decomposition rates of beech litter? (iii) How would the litter decomposing microbial community react to changes in substrate quality during the early stage of decomposition?

To answer these questions, we closely examined nutrient sinks and sources, respiration and microbial community composition weekly over the course of the 8 week experiment. We also focused on interactions between fungi and bacteria in relation to changes in substrate quality, as for terrestrial litter decomposition fungal/bacterial interaction studies (e.g. Rousk and Baath 2007; Schneider et al. 2010) are not as well investigated as in comparison for aquatic litter decomposition (Das et al. 2007; Mille-Lindblom and Tranvik 2003; Schlief and Mutz 2007).

Material and methods

Sample sites

In autumn 2007 we collected fresh beech (Fagus sylvatica L. (1753)) litter from the surface of the forest floor from four different sites in Austria: Achenkirch, Tyrol (AK), Klausen-Leopoldsdorf, Lower Austria (KL), Perg, Upper Austria (PE) and Ort, Gmunden, Upper Austria (OR), which varied in elemental nutrient stoichiometry. Achenkirch is a Tyrolean Spruce-Beech (Picea abies L., H.Karst. (1881)) forest on Rendric Cambisol with a pH of 7.0. The coordinates of AK are 47°35′ N and 11°39′ E (Ambus et al. 2006). Klausen-Leopoldsdorf is situated in the Vienna Woods, with the coordinates 48°07′ N and 16° 03′ E. The soil type is Dystric Cambisol with a pH of 4.6 (Kitzler et al. 2006b). The coordinates of OR are 47°51′ N and 13°42′ E. The forest consists mostly of beech but also of Norway spruce and fir (Abies alba Mill. (1768)). The soil is a Dystric Cambisol on carbonate rock. The coordinates of PE are 14°54′ N and 48° 21′ E. The site is dominated by beech, but it also contains conifers (Picea abies, also Abies alba) and the soil type is Cambisol (eBOD, BFW, Austria, based on the digital soil map of Austria, 1 km-grid).

Experimental setup

Before being put into microcosms, the air-dried beech litter was shredded and sieved with only the fraction from 1 mm2 to 1 cm2 included in the experiment. Every microcosm consisted of a PVC cylinder (Ø 10 × 10 cm height) with a grate two cm above the bottom. Before the start of the experiment, dry litter was re-wetted with deionized water to reach a water content of 60 % and left to equilibrate for 2 days. Into each microcosm 38 g of moist litter were then filled and incubated at 20 ± 2 °C. Water content was adjusted weekly. To prevent drying, the microcosms were covered with a perforated polyethylene foil fixed to a two cm ring of drainage pipe and a moist sponge cloth was laid underneath. After destructive sampling of the microcosms the litter was chopped into small pieces with a two-bladed mincing knife and homogenized.

In total 60 microcosms, 15 for litter from each site were established. Every week respective samples were taken out of one single cylinder, with three exceptions: in week zero samples were gathered from the original litter pool (n = 1). In week four, four microcosms (n = 4), and in week eight five (n = 5) microcosms were available. This approach resulted in an unbalanced statistical design.

The number of subsamples for the used methods is given at the respective section. Gas sampling was conducted every week in four separate microcosms per site. At week eight these microcosms also were analysed for NH4 +, NO3 -, microbial biomass C (Cmic) and N (Nmic) and PLFAs. PLFA analyses were conducted three times during this 8-week experiment, whereas all other analyses were performed weekly.

Elemental analyses

Ten g of fresh litter from every cylinder was oven dried overnight at 105 °C and milled to a fine powder to obtain homogenous samples. Prior to the start of the experiment an elemental analysis of the collected litter with six subsamples per site was performed. The milled samples were wet oxidized (H2SO4 + HNO3) in a microwave oven (Henschler 1988) and elements (P, K, Ca, Mg, Fe, Mn, Zn) were determined by inductively coupled plasma atomic emission spectrometry (ICP-AES). Carbon and N-contents were measured with a Leco CN2000 elemental analyzer (LECO corp. St Joseph, MI, USA) initially and during the experiment for every microcosm.

Gas fluxes

Microcosms were opened 2 min before gas sampling. Two airtight covers were then used for gas sampling, one with a hole and a septum sealed by means of silicone (Baysilone medium viscous; Bayer corp., Leverkusen, Germany) to the top of the microcosm. A 2 mm polyurethane band was adjusted inside the covers and parafilm outside for air-tight sealing. After sealing the microcosms we sampled 10 ml headspace air of the gas tight microcosms. Over a time course of 20 min gas samples were taken every five minutes and gas samples were analysed by gas-chromatography with a 6890 N GC-System (Agilent, Santa Clara, CA, USA) connected to an automatic headspace-sample injection system (Dani HSS 86.50 Dani, Cologno Monzese, Italy). Measurements and calculations of CO2 and N2O were conducted as described by Kitzler et al. (2006a), and of CH4 as in Schaufler et al. (2010).

NH4 ±/NO3 -, Cmic and Nmic; NPOC and TDN –measurement

Litter was extracted in two subsamples per microcosm in a 1:20 w/v ratio in 0.1 M KCl-solution shaking at vigorous agitation for 1 h for analyses of NO3 - and NH4 + which were conducted according to Kandeler (1996) with minor modifications, and detected with a μQuant mQx200 well plate reader (Bio-Tek Instruments, Inc., Vermont, USA) at a wavelength of 660 nm for NH4 + and 210 nm for NO3 -. The determination of Nmic and Cmic was performed from two subsamples per microcosm via chloroform fumigation extraction (Öhlinger 1996). An extractability factor was not applied for calculation of microbial biomass. Non purgeable organic C (NPOC) and total dissolved N (TDN) were determined from a 1:20 w/v extract in 1 M KCl with a TOC/TN analyser (TOC-V CPH E200V, linked with a TN-unit TNM-1 220 V, Shimadzu Corporation, Kyoto, Japan). Sample extracts were stored frozen at −20 °C prior to analysis.

Phospholipid fatty acids PLFA

Phospholipid fatty acid analyses were performed at three time points (initially out of the litter pool (n = 1), after four (n = 4) and 8 weeks (n = 5). For every PLFA analysis, three subsamples were extracted from 1 g subsamples using a modified Bligh and Dyer technique, as described by Hackl et al. (2005), referring to Frostegard et al. (1991) and detected with a FID on a HP 6890 Series GC-System and a 7683 Series injector and auto sampler on a HP-5 capillary column. For identification of the fatty acid methyl esters an external standard (Bacterial acid methyl esters mix from SUPELCO, St. Louis, Missouri, USA) was used. For quantification of the peaks methyl non-adecanoate fatty acid (19:0) was added as internal standard. The GC-injection volume was 0.2 μl. PLFA nomenclature is based upon Frostegard et al. (1993). The PLFAs i14:0, i15:0, a15:0, i16:0, i17:0, a17:0 were chosen to represent gram + bacteria. The PLFAs 16:1ω7, 16:1ω9, 17:1ω9, cy17:0, 18:1ω11, and cy19:0 were used as biomarkers of gram- bacteria (Fierer et al. 2003). 18:2ω6 was regarded as fungal marker (Zelles 1997) and 10Me16:0, 10Me17:0 and 10Me18:0 were applied as biomarkers of actinomycetes (Frostegard et al. 1993). 14:0, 15:0 and 17:0 were for unspecific bacteria. The sum of markers for gram+, gram- and unspecific bacteria accounted for total bacteria. The ratio fungal/bacterial PLFA was calculated with 18:2ω6 divided through the amount of bacterial PLFA (Frostegard and Baath 1996). 20:4ω6 and 20:2ω6 were regarded representative of protozoa (White et al. 1996).

Statistical analyses

One-way ANOVAs followed by Tukey’s HSD test were used to evaluate differences between litter from the four sites. The program used for most of the graphs, regressions and non-parametric Spearman correlations was R 2.9.0 (R_Development_Core_Team 2009). ANOVAs and Pearson correlations of normally distributed data were carried out with STATGRAPHICS Centurion XV (StatPoint Technologies, Virginia, USA). Where a prerequisite for ANOVA in terms of homogeneity of variance was violated, as tested by Levene’s test, non-parametric Kruskal-Wallis tests were calculated. Two principal component analyses (PCA) were calculated with and without PLFA results using the program SIMCA-P 11.0 (Umetrics, Umeå, Sweden).

Results

Litter elemental composition

Beech litter from the four sites significantly differed in nutrient stoichiometry (C:N, C:P, N:P) and in nutrient content for certain elements (P, K, Mg, Mn) as shown in Table 1. Initial C:N-ratios ranged from average 48.7 (AK) to 57.6 (PE) with significant differences between OR, PE and either AK or KL. The C:N-ratios significantly decreased over time in litter from the four sites from 51.8 at week zero to 47.0 at week eight. For single sites, no significant differences were found between the weeks. Averaged over the whole experiment, the highest C:N-ratio was found for PE (51.4), followed by OR (50.3), KL (46.9) and AK (43.9).
Table 1

Initial beech litter chemistry of four different sites

  

AK

KL

OR

PE

C

(%)

50.94 c (0.04)

49.42 b (0.18)

48.65 a (0.14)

49.59 b (0.16)

N

(%)

1.05 c (<0.01)

0.97 b (<0.02)

0.90 a (<0.01)

0.86 a (<0.02)

P

(%)

0.030a (<0.001)

0.033 b (<0.001)

0.045 c (<0.001)

0.056 d (<0.001)

K

(%)

0.099 a (0.002)

0.395 d (0.003)

0.179 b (0.002)

0.212 c (0.002)

Ca

(%)

1.48 c (0.02)

1.30 b (0.02)

2.03 d (0.02)

1.16 a (0.01)

Mg

(%)

0.23 d (<0.01)

0.16 b (<0.01)

0.16 c (<0.01)

0.13 a (<0.01)

Fe

(mg kg−1)

121.9 a (3.7)

175.5 b (4.5)

240.9 c (16.8)

181.5 b (8.5)

Mn

(mg kg−1)

103.8 a (2.6)

985.2 d (18.3)

521.2 c (19.0)

292.8 b (9.4)

Zn

(mg kg−1)

32.3 a (0.9)

39.0 b (1.6)

34.1 a (0.6)

41.2 b (0.8)

C:N

 

48.7 a (0.3)

50.9 a (0.7)

54.2 b (0.4)

57.6 c (0.8)

C:P

 

1707 d (18)

1494 c (34)

1090 b (16)

892 a (2)

N:P

 

35.0 d (0.2)

29.1 c (0.4)

20.1 b (0.2)

15.8 a (0.2)

Values represent means, with +/− SE in parentheses, different letters indicate significant differences between sites after Tukey’s HSD test (p < 0.05, n = 6)

AK Achenkirch, KL Klausen-Leopoldsdorf, OR Ort and PE Perg

Total N content was highest in AK (1.17 %) and lowest in litter from PE (0.93 %) for the duration of the experiment. The initial Mn-, Ca-, P-, and K-contents were significantly different for all sites (Table 1). The initial K-content was highest in KL (0.395 mg kg−1), followed by PE, OR and AK (0.099 mg kg−1). Initial Ca-content was highest in litter from OR (2.03 %) and lowest for PE (1.16 %) and P was highest in PE (0.056 %) and lowest in AK (0.03 %). Manganese was highest in KL (985.2 mg kg−1), followed by OR, PE and AK (103.8 mg kg−1).

Litter mass loss (ML)

Final mass loss was significantly highest with 8.8 % in KL, the site with highest respiratory activity, with 6.6 % in OR, 6.9 % in PE and 7.1 % in AK (Fig. 1a) without significant differences between these three other sites. The cumulative mass loss between week 0 and 1 was slightly negative. This can possibly be contributed to the fact, that watering during the experiment was applied 1 day before the harvesting procedure and thus could have slightly increased the weight of the microcosms. The calculated cumulative C-loss obtained by gravimetric method at week eight ranged between 30.7 mg C g−1 DW (OR) and 43.9 mg C g−1 DW (KL).
Fig. 1

Time course of a) Mass Loss %, b) NH4 +-N, c) total inorganic N (TIN) and d) respiration. An ANOVA was conducted for week 4 and 8 to detect significant differences between the four sites. Where brackets indicate a violation of the prerequisite of homogenous variances, a non-parametric Kruskal-Wallis test was computed (ns p > 0.05; * p < 0.05; ** p < 0.01; *** p < 0.001). AK Achenkirch, KL Klausen-Leopoldsdorf, OR Ort, PE Perg

NH4 ± and NO3 --concentrations

Initial NH4 +-N concentration was highest in AK and lowest in KL, decreased in the first weeks and remained at a low level during the later stage of the experiment (Table 2, Fig. 1b,). Generally NO3 - decreased in the beginning and started to increase after 6 weeks (Table 2). The site highest in respiration (KL) revealed lowest initial NH4 + concentrations but highest concentrations of NO3 -. Total inorganic N (NH4 ++NO3 -, TIN, Table 2, Fig. 1c) declined over time with an initial average of 425 μg N g−1 dw to 66.1 μg N g−1 dw at week eight, and it was generally highest for microcosms of OR (465 μg N g−1 dw to 116 μg N g−1 dw).
Table 2

Nitrogen and carbon pools averaged over all four sites during 8 weeks

Weeks

N

C

NH4 +

NO3 -

TIN

NPOC

TDN

Cmic

Nmic

0

150.8 (4.3)

7421 (66)

4.22 (0.44)

2.16 (0.43)

6.38 (0.10)

147.6 (3.6)

16.33 (0.98)

18.64 (2.67)

1.22 (0.26)

(%)

100

100

2.80

1.43

4.23

1.99

10.83

0.25

0.81

1

155.3 (4.9)

7607 (130)

1.37 (0.19)

2.47 (0.29)

3.84 (0.28)

84.7 (4.8)

9.15 (0.27)

33.20 (1.97)

2.67 (0.23)

(%)

100

100

0.88

1.59

2.47

1.11

5.89

0.44

1.72

2

157.7 (5.3)

7478 (127)

1.46 (0.13)

2.69 (0.22)

4.15 (0.11)

68.3 (4.2)

7.71 (0.13)

45.35 (2.27)

4.92 (0.26)

(%)

100

100

0.92

1.71

2.63

0.91

4.89

0.61

3.12

3

150.0 (5.2)

7309 (122)

0.31 (0.03)

2.29 (0.17)

2.60 (0.16)

58.1 (4.3)

3.59 (0.20)

45.09 (2.25)

4.89 (0.25)

(%)

100

100

0.21

1.53

1.73

0.80

2.40

0.62

3.26

4

151.3 (5.2)

7321 (119)

0.15 (0.02)

1.71 (0.07)

1.86 (0.08)

58.9 (4.1)

3.21 (0.14)

41.52 (2.29)

5.24 (0.30)

(%)

100

100

0.10

1.13

1.23

0.80

2.12

0.57

3.47

5

150.4 (5.5)

7204 (146)

0.40 (0.03)

0.91 (0.05)

1.30 (0.05)

56. (4.9)

3.15 (0.22)

42.98 (0.58)

4.70 (0.17)

(%)

100

100

0.27

0.60

0.86

0.78

2.09

0.60

3.13

6

150.2 (5.7)

7175 (148)

0.38 (0.03)

1.02 (0.11)

1.40 (0.10)

54.4 (4.1)

3.01 (0.17)

53.03 (1.43)

5.30 (0.17)

(%)

100

100

0.25

0.68

0.93

0.76

2.01

0.74

3.53

7

150.6 (5.4)

7128 (158)

0.39 (0.03)

1.55 (0.15)

1.95 (0.15)

59.8 (5.3)

3.80 (0.27)

46.30 (0.63)

4.41 (0.10)

(%)

100

100

0.26

1.03

1.29

0.84

2.53

0.65

2.93

8

148.9 (5.3)

6946 (134)

0.21 (0.04)

0.72 (0.07)

0.93 (0.11)

51.9 (5.2)

2.99 (0.22)

33.95 (2.14)

5.04 (0.24)

(%)

100

100

0.14

0.48

0.62

0.75

2.01

0.49

3.38

Values represent means of all sites with +/− SE in parentheses, units are mg per microcosm. For week zero a litter amount of 15 g dw was estimated, since the samples were taken out of the original litter pool. The percentage is given for each week while elemental C and N analyses were regarded as 100 %

TIN total inorganic nitrogen, TOC total organic carbon, TDN total dissolved nitrogen, C mic microbial biomass C, N mic microbial biomass N (n = 4)

Gas fluxes

Respiration declined during the experiment (Fig. 1d) with highest initial values in KL (104.4 μg C g−1 dw h−1). During most of the experiment respiration was significantly higher in KL than in litter from the other sites and AK was lowest. Respiration showed the opposite pattern than initial NH4 + concentration between sites (KL > PE > OR > AK). During the experiment there was no significant N2O and CH4 production and/or consumption.

The cumulative C-loss after 8 weeks via respiration showed values between 234 mg (AK) and 631 mg (KL). Linear regressions were calculated for C-loss from CO2-respiration and C-loss from the gravimetric mass-loss determination over all time points, with coefficients of determination (r2) ranging from 0.68 (AK) to 0.92 (KL). The final C-loss was on average 1.6-fold higher from the gravimetric (together with the elemental analysis) determination method than from the respiration.

NPOC, TDN, Cmic and Nmic content

Non purgeable organic C and total dissolved N (NPOC and TDN, Table 2, Fig. 2a and b) decreased over time, with significant differences between litters from different sites at week four and eight. A strong positive relation between NPOC and TDN was observed by single linear regression analysis (p < 0.001, r2 = 0.78). The time course of NPOC was similar to that of TDN, except that TDN was highest for KL and lowest for AK most of the time. The C:N ratio of non purgeable organic C and total dissolved N (NPOC:TDN) increased between start and end of the experiment for all sites, and its time course is shown in Fig. 2c.
Fig. 2

Time course of a) non-purgeable organic carbon (NPOC) b) total dissolved nitrogen (TDN), c) ratio of NPOC and TDN (NPOC:TDN). The remaining graph d) represents a quadratic regression between TDN and the cumulative litter mass loss (ML). The values for TDN between b) and d) differ, since the first week was not used for mass loss determination. The outer lines of the regression curves indicate a 95 % confidence interval and the line in between is the regression line. An ANOVA was conducted for week 4 and 8 to detect significant differences between the four sites. (ns p > 0.5; * p < 0.05; ** p < 0.01; *** p < 0.001). AK Achenkirch, KL Klausen-Leopoldsdorf, OR Ort, PE Perg

Linear regression analysis for each separate site revealed a strong positive correlation between DON (= TDN - inorganic N) and NH4 + (with r2-values ranging from 0.60 (KL) to 0.97 (AK)) as well as a quadratic regression revealed a strong correlation between TDN and mass loss calculated with mean values over all sites (Fig. 2d, n = 30, without week zero and two slightly negative values after week 1, p < 0.001, r2 = 0.66). Also for NPOC:TDN and litter mass loss (p < 0.001, r2 = 0.57) a quadratic regression was calculated.

For Cmic and Nmic (Table 2, Fig. 3a and b) a similar trend over time was observed and they were well correlated (Spearman’s r = 0.66, p < 0.001). Initially Nmic was low but sharply increased in the first 3 weeks. For the time period of the whole experiment Cmic and Nmic mostly was highest in KL. The C:N-ratio in microbial biomass (Cmic:Nmic) declined over time starting from 18.1 in week 0 to 6.7 in week 8 for all sites. The course of Nmic inversely matched the development of NH4 + and a highly significant quadratic relationship was observed between those two variables (Fig. 3c). This suggests that most of the NH4 + was incorporated into microbial biomass within the first week.
Fig. 3

Time course of a) microbial biomass C (C mic ), b) microbial biomass N (N mic ) and d) the microbial metabolic quotient (qCO 2 ). The remaining graph c) represents a quadratic regression between NH4 + and microbial biomass N (N mic ). The outer lines of the regression curves indicate a 95 % confidence interval and the line in between is the regression line. In a) and b) an ANOVA was conducted for week four and eight to detect significant differences between the four sites, and in d) for week eight only. Where brackets indicate a violation of the prerequisite of homogenous variances, a non-parametric Kruskal-Wallis test was computed (ns p > 0.5; * p < 0.05; ** p < 0.01; *** p < 0.001). AK Achenkirch, KL Klausen-Leopoldsdorf, OR Ort, PE Perg

For receiving a short outline, we provided a table with the examined nutrient pools during the time course of the experiment (Table 2). The proportions of NPOC (below 2 %), TDN (below 11 %), Cmic (below 1 %) and Nmic (below 5 %) in comparison to the total C and N amount of the litter, averaged for litter across all sites, were small.

Microbial metabolic quotient

The microbial metabolic quotient (qCO2) was calculated as respiration divided by Cmic (Fig. 3d). A decrease during the time course of the experiment was observed with values ranging from 45.34 mg CO2-C g−1 Cmic h−1 (KL) at week zero to 1.98 (AK) at week eight. Since respiration was measured in four separated microcosms, an ANOVA only was conducted for week eight. A strong positive relationship for the duration of the experiment over all sites was noticed between qCO2 and log-transformed TIN (p < 0.001, r2 = 0.70).

Multivariate relationships

A PCA was computed with NO3 -, NH4 +, respiration, NPOC, TDN, Cmic and Nmic (Fig. 4a). The arithmetic mean was calculated for each harvest time point and litter site, resulting in 36 observations and seven variables. The analysis revealed two principal components (PC). The first one was mainly explained by TDN (+0.94), Nmic (−0.80), NH4 + (+0.75) and Cmic (−0.51) and it explained 59.6 % of the variation and the second one by NO3 - (+0.71), respiration (+0.56) and Cmic (+0.26) and it explained 23.8 % of the variation. Figure 4a shows that “time” was a more separating factor than “site”, indicating the stronger influence in comparison. Respiration and consequently litter mass loss was also influenced by TIN, as a strong negative correlation between TIN and Litter mass loss was detected (p < 0.001 and r2 = 0.60). As expected, TIN was correlated with NPOC:TDN (p < 0.001, r2 = 0.56) and furthermore with TDN (p < 0.001, r2 = 0.71), since the TIN provides a portion of the TDN ranging from 19 % (KL, week 8)– 92 % (PE, week 3) over the whole experiment. Litter from KL showed the highest respiration and lowest NH4 +-concentration.
Fig. 4

Principal component analysis of a) inorganic N-pools, CO2-respiration, non purgeable organic C, total dissolved N, microbial biomass C and N and b) the same variables including PLFA data. Data points (indicated as numbers in a) and points in b) represent litter samples from four sites at a) eight (week 0–8), respectively b) three (week 0, 4, 8) time points. The variables NH4+, NO3 -, CO2 and Nmic have been log-transformed prior to PCA calculation for a) and NO3 - and CO2 for b). Explanation of variance by means of individual principal components (PC) is shown through percentages. Numbers indicate time and letters sites. C mic microbial biomass C, N mic microbial biomass N, TDC total dissolved C, TDN total dissolved N, A Achenkirch, K Klausen-Leopoldsdorf, O Ort, P Perg

Phospholipid fatty acids

PLFA data were classified into actinomycetes, fungi, gram + and gram- bacteria, and protozoa. Initially gram- bacteria dominated all microbial communities and only fungi and actinomycetes were significantly different among litters from different sites (Table 3). The total amount of PLFAs did not change significantly with time. In week zero 54.3 nmol g−1 dw ± 0.7 (SE, n = 12), in week four 56.1 ± 0.6 nmol g−1 dw (SE, n = 16) and in week eight 53.3 ± 1.4 nmol g−1 dw (SE, n = 20) total PLFAs were measured.
Table 3

PLFA-contents of beech leaf litter of four sites at three time points

Site

Actinomycetes

Fungi

Gram +

Gram -

Protozoa

FB-Ratio

Total PLFAs

Week 0 (n = 3)

AK

0.20 b (0.03)

9.93 (0.76)

1.20 a (0.09)

42.41 (2.70)

0.264 (0.022)

0.22 (0.03)

54.01 ab (1.81)

KL

0.01 a (0.01)

7.63 (0.09)

0.83 a (0.01)

42.39 (0.10)

0.213 (0.014)

0.17 (<0.01)

51.07 a (0.08)

OR

0.29 b (0.04)

9.62 (0.71)

2.54 b (0.23)

43.54 (1.75)

0.318 (0.042)

0.20 (0.02)

56.31 b (0.76)

PE

0.21 b (0.03)

8.71 (0.16)

1.30 a (0.05)

45.28 (0.28)

0.329 (0.016)

0.18 (<0.01)

55.83 b (0.04)

Week 4 (n = 4)

AK

0.32 c (0.01)

18.12 ab (0.25)

4.41 c (0.13)

30.60 a (0.75)

0.499 b (0.018)

0.48 (0.01)

53.95 (0.85)

KL

0.24 b (<0.01)

18.27 ab (0.17)

3.85 b (0.06)

34.28 b (0.95)

0.427 a (0.005)

0.45 (0.01)

56.74 (1.47)

OR

0.41 d (<0.01)

18.77 b (0.17)

7.11 d (0.15)

31.05 ab (0.88)

0.466 ab (0.004)

0.46 (0.01)

57.81 (0.95)

PE

0.22 a (<0.01)

17.68 a (0.27)

3.36 a (0.09)

33.50 ab (0.74)

0.892 c (0.023)

0.45 (0.01)

55.65 (1.00)

Week 8 (n = 5)

AK

0.37 ab (<0.01)

18.88 (0.08)

5.35 a (0.10)

28.69 a (0.46)

0.474 a (0.026)

0.52 (<0.01)

52.93 a (0.09)

KL

0.42 b (0.03)

18.33 (1.49)

4.79 a (0.20)

30.90 bc (0.59)

0.491 a (0.015)

0.48 (0.05)

54.92 ab (0.75)

OR

0.71 c (0.02)

17.05 (1.09)

9.14 b (0.37)

29.68 ab (0.37)

0.489 a (0.013)

0.45 (0.04)

57.39 b (0.60)

PE

0.31 a (0.01)

16.72 (1.16)

5.28 a (0.30)

31.79 c (0.56)

0.858 b (0.055)

0.42 (0.04)

54.96 ab (0.58)

Accounting PLFAs have been summed up to result in the assigned groups. Values represent means with +/− SE in parentheses, different letters indicate significant differences between sites for each time point after Tukey’s HSD test (p = 0.05). Except for FB-Ratio all units are nMol PLFA g−1 dw

FB-Ratio fungal bacterial ratio, AK Achenkirch, KL Klausen-Leopoldsdorf, OR Ort, PE Perg

However, the relative abundance among certain microbial groups did change with time, as revealed by one-way ANOVA. During the first 4 weeks of the experiment all groups except gram- bacteria increased. This was followed by a general decrease of all groups, with exception of actinomycetes and gram + bacteria, which both increased in microcosms of OR. For litter from all sites fungi increased in the first 4 weeks. From weeks four to eight, however, differences in the relative abundance of different microbial groups differed among litters. For example: litter of two sites gained in fungal biomass (AK, KL) while other two declined (OR, PE). In general, however, fungi showed a significant increase throughout the course of the experiment (ANOVA, p < 0.001).

In the microcosms of OR most gram + bacteria and actinomycetes were found. Gram- bacteria decreased on average for all sites from 43.4 nmol g−1 dw at week zero to 30.3 nmol g−1 dw in week eight, and fungi increased from 9.0 to 17.6 nmol g−1 dw. Significant correlations were observed for gram + bacteria and actinomycetes (Pearson’s r = 0.91, p < 0.001), gram + with gram- bacteria (Spearman’s r = −0.83, p = 0.001) and fungi and gram- bacteria (Spearman’s r = −0.75, p < 0.01). A PCA was conducted, containing all the PLFA groups and respiration, NH4 +, NO3 -, TDN, NPOC, Nmic and Cmic (Fig. 4b). Prior to performing the PCA, the arithmetic mean of the data set was calculated for three harvests (at week zero, four and eight) and each site, resulting in 12 observations and 11 variables. Three PCs were calculated. The first PC was mostly explained by gram- bacteria (+0.97), fungi (−0.94), TDN (+0.90), NPOC (+0.89), NH4 + (+0.82), Nmic (−0.80), gram + bacteria (−0.69) and Cmic (−0.51), the second one by CO2 (+0.53), NO3 - (+0.52) and actinomycetes (−0.31). The third PC explained less than 1 % of total variation. Microbial community composition changed rapidly within the first 4 weeks, whereas only minor shifts occurred from week four to eight. The PCA also showed, that week zero was more different than week four and eight, which grouped closer together (Fig. 4b).

The results of the PCA revealed certain interrelationships between microbial community data and litter chemistry. Actinomycetes and fungi were negatively correlated with respiration. Gram- bacteria were positively and fungi negatively correlated with TDN and NH4. The concentration of NH4 + was highest in the first week and decreased over time as did gram- bacteria. Factors on the qCO2 cannot be easily derived from the picture. There was a strong negative correlation between fungal-bacteria ratio (FB-ratio) and Cmic:Nmic (Spearman’s r = −0.62, p < 0.05) as well as with TIN (Spearman’s r = −0.59, p < 0.05). The qCO2 was strongly negatively correlated with fungi (Spearman’s r = −0.75, p < 0.01) and positively with gram- bacteria (Spearman’s r = 0.85, p < 0.001).

Discussion

The influence of N on early beech litter decomposition (i)

During the early stages of decomposition, microbial N limitation can occur due to high microbial N demand (Moorhead and Sinsabaugh 2006). Thus, N is proposed to accelerate decomposition during the early stages. We were not able to explain differences in respiration rates among leaf litter from the four sites neither by initial bulk N-content nor by bulk C:N-ratios as suggested by Zhang et al. (2008), which would negate our first research question (i) “Would higher initial N content and lower C:N ratios accelerate early stage decomposition rates?”. This might be attributed to the fact, that the present experiment is using only litter from four sites for evaluating this impact. We therefore tried to evaluate different influencing factors on respiration.

A strong correlation of respiration and C:N ratio of soluble C and N was observed in previous decomposition studies dealing with tropical tree species (Bernhard-Reversat 1999), and also for a decomposition study from temperate climate (Marstorp 1996). In addition, it has been mentioned, that mainly soluble carbon compounds (e.g. unshielded cellulose and hemicelluloses) are decomposed in early stages of decomposition (Berg and McClaugherty 2008). Since beech litter is considered to be rich in lignin (Melillo et al. 1982), a larger portion of cellulose may be shielded, increasing the influence of the soluble fractions during decomposition. We found that respiration was highest in leaf litter of sites with a high NPOC:TDN ratio, which is consistent with the idea, that this soluble C:N is a good an indicator for the degradability of litter organic compounds (Bernhard-Reversat 1999). The influence of soluble compounds on litter decomposition has often been underestimated compared to litter chemistry stoichiometry (Cleveland et al. 2004). Moreover, an amount of about 10–40 % of DOC (comparable with our NPOC) is quickly degraded within a period of days to a few months (Silveira et al. 2011). Our results emphasize the relationship between soluble compounds and respiration although the relationship between litter mass loss and TDN was even stronger.

Higher microbial N demand (Moorhead and Sinsabaugh 2006) at early stages was on the one hand supported by our study by the quick incorporation of NH4 +, on the other hand a limitation of total N was not observed. The strong influence of soluble C and N together on this early stage decomposition could possibly explain this discrepancy: the bulk C:N ratio in our beech litter was about fourfold higher than the more influencing NPOC:TDN. In comparison to the Cmic:Nmic the NPOC:TDN was quite low in the beginning (0.6) and increased to 2.6 after 8 weeks for all sites. The bulk C:N stayed at a comparably similar level between start and ending of our experiment. This comparison supports the observation, that the bulk N of our litter was not limiting decomposition after 8 weeks (i). There was no correlation found between the bulk C:N and NPOC:TDC. The linear correlation of TDN with mass loss was strongly affected by the factor time, and sites neither highest in TDN nor in NPOC showed highest respiration in the beginning, where respiration was highest, whereas this was the case for the NPOC:TDN ratio.

Since the litter from the site KL showed the highest respiration and lowest NH4 +-concentration, previous immobilization of NH4 + may have led to a negative relationship between NH4 +-concentration and respiration. On the contrary we observed that sites with a higher proportion of NH4 + in the TIN-pool resulted in lower respiration rates.

These findings are supported by quick incorporation of NH4 + that supposedly resulted in a transformation from inorganic N to organic amino compounds like glutamate or glutamine (Tempest and Meers 1973). This further indicates that although inorganic N, mainly as NH4 +, might have been utilized by microbes and accumulated into microbial biomass, higher amounts of NH4 + did not accelerate microbial respiration. This effect had been mentioned earlier as “ammonia metabolite repression” of fungi (Keyser et al. 1978). In the previous study NH4 + was reported to be able to repress the synthesis of some fungal enzymes. If this mechanism for repressing expression of enzymes can also occur in a bacterial dominated microbial community our observation would indicate that the inhibition of fungal derived enzymes would slow down litter decomposition rates already at an early stage, as they are considered to be the main degraders of recalcitrant compounds in leaf litter (Keyser et al. 1978).

On average the gravimetric C-loss was 1.6-fold higher than C-loss via CO2 after 8 weeks (the average r2 for each site was 0.83). We expected a similar C-loss between both methods for calculating C-loss. A possible reason for the observed differences could be that mass loss was obtained from all microcosms after destructive sampling, calculated on a dry weight basis, whereas C-loss from respiration was measured in four particular microcosms only. Regardless the gravimetric and respiration C-loss were related with a 1.6-fold factor, this is a clear indication, that respiration and mass loss are closely coupled together and respiration is an appropriate measure for decomposition rate.

The influence of initial elemental content (ii)

Other nutrients beside N could have had an effect on decomposition rates. For example Mn as a component of the exoenzyme manganese peroxidase (Berg et al. 2007) could have been an important driver for decomposition, as litter from the most active site K also showed the highest Mn content. Also P, which is known to be able to influence decompostion rates (Hobbie and Vitousek 2000) as well as Calcium (Ca), since Ca stabilizes plant cell walls (Demarty et al. 1984) and therefore possibly reduces accessibility to nutrients for microbial decomposers.

While for Austrian beech litter a critical value of 1800 for the C:P ratio was proposed, above which P mineralization is inhibited (Mooshammer et al. 2011), the highest measured C:P ratio in this study was only 1708 for AK. Therefore it is assumed, that P was not limiting litter decomposition in this experiment, although litter from AK had lowest P concentration as well as respiration rates. This indicates, that the P concentration still might have influenced microbial activity – but as AK and OR showed highest concentrations of Ca and lowest respiration, a negative influence of Ca on decomposition also cannot be excluded. Potassium was significantly highest in concentration at the most active site KL. In addition it has been shown previously that the K-content stimulated litter decomposition rates of living plants of several British herbaceous species (Cornelissen and Thompson 2008). However, litter from four sites varying in several elemental concentrations might be too less to get clear predictions of a single element on litter decomposition rates.

Microbial community changes (iii)

Gram- bacteria initially dominated the microbial community followed by gram + bacteria and fungi, although gram- bacteria continued to have the highest relative abundance until the end of the experiment. These results matched our expectations, as bacteria are in general able to degrade easily accessible compounds available during the initial stages of decomposition. Bacteria in general are followed by an increase in fungal abundance after depletion of the easily accessible compounds as fungi are more specialized on the more recalcitrant fraction for degradation (Bossuyt et al. 2001; McMahon et al. 2005; Six et al. 2006). Similar shifts in microbial community composition were recorded for soils under an alpine grass and during primary succession (Tscherko et al. 2004), where initially dominating gram- bacteria were later succeeded by gram + bacteria and fungi. However, the data from this experiment were exclusively derived from decomposing leaf litter excluding soil. In addition the time course of this experiment was much shorter than most other decomposition studies and we assume that the substrates available in the early stages of decomposition will differ widely from most other decomposition experiments and also from litter studies including soil.

Whereas chloroform fumigation data indicated a steady increase in microbial biomass, there was no increase in total PLFA amount over the whole experiment. While in studies from other terrestrial decomposition experiments (Bailey et al. 2002; Leckie et al. 2004) a correlation between PLFA data and microbial biomass by chloroform fumigation was found, this was not observed in the present study. Bacteria are believed to contain more PLFAs per g biomass compared to fungi (Joergensen and Wichern 2008) and the observed decrease of gram- bacteria and increase of fungi over time could be another reason for not finding a correlation between total PLFA and Cmic.

It would have been expected, that with increasing fungal biomass, the microbial C:N ratio would increase as well (Campbell et al. 1991). In the present study, the Cmic:Nmic decreased, averaged for all sites, over time from 19.0 in week zero to 6.6 in week eight. The relatively high ratio in week zero could possibly be addressed to dormant bacteria or fungal spores, since soil microorganisms appear to be largely dormant (P. Lavelle et al. 1995). The reason for the lower ratio in the last week may be due to a decrease of Cmic. Except for these 2 weeks the ratio seemed to be quite stable during the overall experiment (the average for all sites from week two to week eight was 9.9), which would be in accordance to the demand of microbial homeostasis (Cleveland and Liptzin 2007). Even though it has been mentioned that indicators for fungal biomass were correlated with Cmic:Nmic ratio (Joergensen and Emmerling 2006), this was not always the case (e.g. Salamanca et al. 2002).

As microbial biomass was not related to respiration and litter mass loss in our experiment, the metabolic quotient (a combined factor) might provide a better insight. In the early stages respiration was highest, but microbial biomass was lowest indicating a decrease of qCO2 over all sites during the time course of the experiment. This is consistent with the decrease of the gram- bacteria and the increase of fungi, which are supposed to have a lower qCO2 (Grayston et al. 2001). Moreover, the coherency of qCO2, TIN and FB-ratio also indicated an impact of easily available N, primarily of NH4 +, on qCO2. Higher values of qCO2 in earlier stages of this experiment with a higher gram- bacteria content indicated lower C-use efficiency. During later stages with increased dominance of fungi, the C-use efficiency would be expected to increase (Keiblinger et al. 2010), and therefore qCO2 to decrease. Litter quality was already defined as a decreasing function of time elsewhere (e.g. d’Annunzio et al. 2008), and in our early stage experiment many observed effects between the sites were superposed by the occurring time course. A previous study also observed that the combined “time-site” factor had a highly significant effect on extracellular enzyme activities (Keiblinger et al. 2012), which are of major importance in the breakdown of recalcitrant plant material. The response time of the microbial community was remarkably short: The microbial community structure significantly changed between week zero and week four. The differences between week four and week eight were only in a minor range in comparison indicating an early adaptation from week zero to week four. The already noticeable succession towards fungi after 4 weeks confirmed a strong impact of the more labile C- and N-pools on the microbial community as well as on the process of litter decomposition, since those pools also underwent the most pronounced changes in the very first weeks of the experiment in accordance with the stronger microbial respiration at the beginning.

Conclusions

During early stages of litter decomposition, the influence of NPOC:TDN on litter decomposition had a stronger effect on decomposition rates than bulk elemental stoichiometry. While it was expected that beech litter enriched in total N would show highest decomposition rates, in the present experiment this was not the case. Furthermore, sites lower in initial NH4 +-N showed less respiration and mass loss. Initial beech litter elemental contents could have a possible influence on decomposition since Mn and K, were highest in litter from the most active site (KL) and lowest in litter from the least active site (AK).

This experiment was unique in terms of temporal resolution in early stages of litter decomposition combined with a broad range of measurements of ecophysiological parameters. Over this short period of incubation (8 weeks), our data suggest that the factor “time” was more predictive than the factor “site”, indicating that major changes occurred during the initial stages of decomposition, as had been observed for the microbial community structure. The shift from a generally bacterial dominated regime to an increase of fungal PLFAs was related with a decrease of respiration and an increase in metabolic quotient.

Notes

Acknowledgments

This research was performed within the National Research network MICDIF (S100) of the Austrian Science Fund FWF (Project numbers S10001, 2, 3, 4, 6, 7-B17). Special thanks to Michael Pfeffer for assistance with PLFA analysis, to Helene Hochrieser for statistical assistance, to Barbara Kitzler with the gas-measurements and to Brigitte Schraufstädter and Veronika Bendl for laboratory assistance. We are highly grateful to Marianne Koranda for support with the Cmic and Nmic measurements and to Alfred Fürst for measurements of element concentrations. We also want to express our gratitude to Edward K. Hall for a final proofreading of the manuscript. The Austrian Academy of Sciences (ÖAW) granted Katharina Keiblinger with a DOC-fFORTE fellowship.

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© The Author(s) 2013

Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • Christian Brandstätter
    • 1
    • 4
  • Katharina Keiblinger
    • 2
  • Wolfgang Wanek
    • 3
  • Sophie Zechmeister-Boltenstern
    • 2
  1. 1.BFW – Federal Research and Training Centre for Forests, Natural Hazards and LandscapeViennaAustria
  2. 2.Institute of Soil ResearchUniversity of Natural Resources and Life SciencesViennaAustria
  3. 3.CHECO – Institute of Chemical Ecology and Ecosystem ResearchUniversity of ViennaViennaAustria
  4. 4.Institute for Water Quality, Resources and Waste ManagementVienna University of TechnologyViennaAustria

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