Decomposition dynamics of coarse woody debris of three important central European tree species

Decomposition rates (k) and mass loss

To our knowledge, this is the first study that systematically assessed CWD decomposition rates and dynamics of Fagus sylvatica, Picea abies and Pinus sylvestris across different sites and diameter classes in Central Europe. Average k values of F. sylvatica were significantly higher than those of P. abies and P. sylvestris across all sites and diameter classes.

In other studies, decomposition rates of F. sylvatica varied from 0.056 year⁻¹ (Kahl 2008) to 0.089 year⁻¹ (Müller-Using and Bartsch 2009), with intermediate values of 0.06–0.075 year⁻¹ (Christensen et al. 2005; based on 86 European beech forest reserves). In comparison to these findings, our k value for beech of 0.054 year⁻¹ (SD ± 0.028, 52 %) is within the range of variation and very similar to the estimate by Kahl (2008).

The difference between k values of beech may be attributed to the uncertainty over the cause of death, as observed by Kahl (2008). In that study, a k value of 0.075 year⁻¹ (SD ± 0.034) for logs that died naturally (infected by Fomes fomentarius prior to death) and a k value of 0.025 year⁻¹ (SD ± 0.012) for wind thrown logs was calculated (mean diameter > 40 cm). This indicates that decomposition had advanced already in trees affected by F. fomentarius before they died or fell. We calculated a k value of 0.035 year⁻¹ (SD ± 0.015) for the diameter class > 40 cm; which is similar to the estimate by Kahl (2008). Most of the trees died “naturally” due to F. fomentarius in the study by Müller-Using and Bartsch (2009), which may mainly explain the high k value in that study. It can be assumed that most logs investigated in our study originated from trees that were still vital compared to trees that died “naturally”. In freshly fallen logs, colonization by (cord-forming) basidiomycetes differs from logs that have already been affected by fungi before they fell and wood can be expected to decay slower (Boddy and Heilmann-Clausen 2008). Further, tissues that have died “naturally” may be predisposed to microbial colonization, whereas artificially detached living tissues (i.e., due to wind breakage) may maintain metabolic activities against decomposers (see Yin 1999). In addition, different k values might result (to some extent) from different tree diameters in other studies. Colonization by (cord-forming) basidiomycetes seems to progress slower in large diameter logs (Boddy and Heilmann-Clausen 2008). Although this is not supported by the results of this study, different site, i.e., microclimatic conditions might further lead to an increase or decrease in k values, for example through canopy removal (Hagemann et al. 2010; Forrester et al. 2012). Managed forests may also harbor a lower diversity of wood decaying fungi, which may lead to different decay rates (Stenlid et al. 2008; Purahong et al. 2014a, b). With regard to the origin of dead wood and its diameters, our data are likely more representative of managed forests rather than strict reserves.

The decomposition rate of 0.033 year⁻¹ (SD ± 0.013, 39 %) for P. abies calculated in our study was comparable to k values in other studies (see Rock et al. 2008). In detail, it was in the range of variation of the estimate by Kahl (2003; 0.027 year⁻¹ (SD±0.023)) in Central Germany and identical to k values found by Naesset (1999; k = 0.033 year⁻¹; mean diameter: 13 cm) in southeastern Norway. It was also similar to values in European (Shorohova and Kapitsa 2014) and Russian boreal forests (Krankina and Harmon 1995; Krankina et al. 1999; Tarasov and Birdsey 2001; Harmon et al. 2000; Yatskov et al. 2003).

The conditions of the decomposition process in the study by Naesset (1999) were very similar to our situation. Their starting point of the decomposition process was the date of cutting. All logs were free from rot at the time of cutting and the decomposition took place in open areas.

Compared to beech, decomposition rates k from the literature are much less variable for spruce. One explanation for this may be that the sensitivity to influencing factors (as cause of death, decomposer community or differences in diameter) is less pronounced in spruce. This may partly be attributable to a higher decay resistance of spruce and more uniform substrate conditions (see also Cornwell et al. 2009; Weedon et al. 2009) when compared to beech.

However, we observed a significant effect of diameter on k values also for spruce. Since all comparable studies in boreal regions were conducted mainly on small diameter trees, the variation between k values should also be small. In our study, the influence of climatic variables on the decomposition, also of spruce logs, was low. For example, we found no difference between k values for the sites Kib (‘cold and wet’) and Sig (‘warm and dry’), which may simply reflect different types of climatic limitations for wood decaying fungi when compared to the ‘warm and wet’ optimum, which was not represented in our design.

Our k value for P. sylvestris (0.032 year⁻¹ (SD ± 0.015, 47 %)) was similar to values found in the boreal forests of Europe (Shorohova and Kapitsa 2014) and Russia (Krankina and Harmon 1995; Harmon et al. 2000; Wirth et al. 2000; Yatskov et al. 2003; see also Rock et al. 2008).

The k value of pine determined in our study was mainly attributable to the loss of sapwood (as heartwood was found to be still largely intact for most logs after 36 years of decomposition time). Hence, if a longer total decomposition period was analysed, the average decomposition constant might be lower than the one we reported here.

Similar to spruce, variation in the decomposition constant k of pine logs was also very low. This may again point to a higher decay resistance and a more uniform decomposition process. In the gymnosperm wood of spruce and pine, there was no highly variable spatial pattern of intact and highly decomposed patches next to each other, as was observed for beech. This observation is in accordance with much higher variation in log respiration rates in dead wood of F. sylvatica than in P. abies and P. sylvestris (Herrmann and Bauhus 2012).

At a more general level, k values of angiosperm wood are typically higher, on average 77 %, than in gymnosperm wood (Weedon et al. 2009). A similar difference was also found by Russell et al. (2014). For comparison, our decomposition rate for beech CWD was about 60 % higher than for spruce and pine.

An estimation of decomposition rates for the federal state of Brandenburg (Northeastern Germany) based on a literature review and expert consultation (Rock et al. 2008) produced k values for F. sylvatica, P. abies and P. sylvestris of 0.067 year⁻¹, 0.0525 year⁻¹ and 0.0575 year⁻¹, respectively. In comparison to our results the estimated k values of P. abies and in particular of P. sylvestris appear to be too high. The deviations between expert estimates of decay rates and our data underpin the importance of actual measurements. In particular for low decay rates, small absolute errors result in proportionally very large errors when calculating mass loss. For example, using a k value of 0.0525 year⁻¹ for Picea abies (Rock et al. 2008), which is 59 % higher when compared to 0.033 year⁻¹ (determined in this study) would shorten the period until 50 % of CWD mass was lost by about 37 % (8 years).

In contrast, the chronosequence approach used in our study may cause an underestimation of k, because slow-decaying logs may have a higher probability of being included in the sampling (Kruys et al. 2002; Herrmann and Prescott 2008). However, this problem increases with the decomposition time covered and it is negligible, when the observation period is shorter than the minimum decomposition time for logs. For example, if we assume a) high decomposition rates of k = 0.09, and b) that logs can still be identified in the field when they contain 20 % of the original mass, no logs should be lost form the sample population before 18 years.

In our study, decomposition rates increased with decreasing diameter class, except for P. abies, where the k values between 20 and 40 cm tended to be slightly higher than in smaller logs (<20 cm) (Table 4). Lower k values for smaller diameters (< 10 cm compared to > 25 cm) of P. abies were also observed by Naesset (1999), who assumed that it was most likely caused by branches that prevent the logs from soil contact. In our study, the diameters < 20 cm for P. abies were most often sampled in crown sections of the logs, where also soil contact was less often encountered than for 20–40 cm diameter logs. However, k values commonly decrease with increasing diameter (Graham and Cromack 1982; Stone et al. 1998; Chambers et al. 2000; Mackensen et al. 2003), even if sometimes inconsistent relationships have been reported (see Herrmann and Prescott 2008). Hence, where decomposition models for CWD with large variation in dimensions are required, it is advisable to consider log diameter.

We are aware that our assumption of a constant decay rate based on the single negative exponential decay model may be questionable and that there are more sophisticated models, e.g., as applied in the Yasso model (Tuomi et al. 2011). However, our dataset was not sufficient in terms of the variation in decomposition periods of logs to fit the temporal decay dynamics of a more sophisticated model in a robust way. Since there are only very few field experiments that followed mass loss in individual CWD pieces over the long term, the ‘real’ temporal mass loss pattern is not known. Even if, the mass loss rate of some wood constituents slowed down with increasing time, this may be partially compensated by fragmentation, which was found to be of increasing importance in later decomposition stages (see e.g., Lambert et al. 1980). The mass loss rate may even increase in later stages as observed in the study by Kahl (2008).

Prediction of mass remaining

A substantial proportion of the variation (64 %) in mass remaining could be predicted by decomposition time, tree species, original log diameter, and their interactive effects, whereas temperature had a very small, and initial nutrient concentrations had almost no influence. The observed influence of manganese concentrations on mass remaining may be explained by the relevance of manganese for lignin degradation by white-rot fungi (Hofrichter et al. 2009).

Similar to our findings, a literature review on CWD decomposition had indicated that tree species had a stronger influence on decomposition and nutrient dynamics than the abiotic environment (Laiho and Prescott 2004). And no effect of lignin or nitrogen concentrations was observed in an attempt to model CWD decay based on data about 300 cases of stem, branch and root woody debris decay from North America (Yin 1999). Also, most of the explained variation of mass remaining in the study by Yatskov et al. (2003) could be attributed to time since death (50 %), log position (8 %) and tree species (6 %). However, in some studies, environmental variables did explain a significant proportion of CWD decomposition over time (e.g., Russell et al. 2014). About 80 % of the variance in the decomposition rate constant was explained by a multiple regression model with the factors of tree species, mean diameter, mean temperature in July, sum of precipitation per year and a lag time (Zell et al. 2009).

In contrast to Zell et al. (2009), we observed no significant improvement in our prediction if mean temperature in July instead of mean annual temperature was used in the model.

In a global meta-analysis of wood decomposition rates of angiosperms and gymnosperms, significant relationships between wood traits and decomposition rates were only observed for angiosperms (Weedon et al. 2009). For angiosperms, positive relationships between k and the nutrients N and P, and a negative relationship between k and C:N ratios were found. We suggest that the mass loss of CWD of the species investigated in our study can be sufficiently well predicted for most management and modelling tasks by the factors tree species, time since commencement of decomposition, and log diameter, which can be obtained easily from forest inventories. Predictions will likely become less accurate, when CWD originates from different processes, e.g., when trees also die standing and decay slowly for many years before logs hit the ground, or when the wood decomposition by fungi commences in the living tree (e.g., Kahl 2008). In our field study, most trees were felled as living trees by storms.

In contrast to this field study, about 60 % of the variation in CO₂ flux of CWD of the same species was explained by climatic variables (wood moisture and wood temperature) in a lab incubation experiment (Herrmann and Bauhus 2012). In the same study, temperature explained more than 90 % of CWD respiration of individual Fagus sylvatica and Picea abies logs over one year in the field. This comparison shows that scaling up from short-term CWD respiration measurements to the long-term dynamics of CWD mass loss is difficult, since the combined effects of temperature, moisture, and interactions between substrate quality and microorganisms need to be considered (Herrmann and Bauhus 2008). In order to capture the complex interplay of processes (i.e., respiration, fragmentation) responsible for decomposition in forests, long-term field measurements are necessary.

CWD fragmentation

The limited study on wood fragmentation indicated that this process may contribute considerably to annual mass loss (max. 30 % of k for pine) although values were lower than in some other studies (e.g., 63 % found by Lambert et al. 1980). Our logs were mainly at the beginning and in the middle of the decomposition process. Fragmentation was found to be of increasing importance with advanced decomposition (Harmon et al. 1986; Müller-Using and Bartsch 2009) as well as in higher elevations (Lambert et al. 1980). However, our study also showed that fragmentation was highly variable within and between logs, and that single events, such as activities of animals searching for food in the logs, may contribute substantially to mass loss.

Measuring fragmentation over the course of only one year was certainly not sufficient to assess the significance of this process in the medium to long-term. This would deserve a separate study. However, it would be very difficult to include fragmentation in models predicting CO₂ release from CWD decomposition, since a large proportion of the material lost from CWD through fragmentation is simply transferred to a different pool, the litter layer. And we have no information on the decomposition rate of fragmented wood in the litter layer.

Drill resistance measurements

In a study that used a similar measurement device, 65 % of the variation in wood density of P. abies could be explained by drill resistance (Kahl et al. 2009). The potential to determine wood density in a large number of samples in a short period of time may compensate for the lack of precision for individual pieces in large inventories. In addition, determining drill resistance may be the only form to collect data on wood density in strict forest reserves, where the collection of stem discs is not permitted.

Predicting carbon density in CWD

The loss of mass and thus carbon in CWD is a continuous process. Using distinct decay stages based on visual assessment of logs can be a useful approach to capture the variation in CWD mass and C density in forest inventories. However, the usefulness of this approach depends on how distinct decay stages differ in wood density and C concentration.

Here, we observed a high variation in CWD density within decay classes and hence only few significant differences between adjacent decay classes within a given species. Density variation was particularly high for F. sylvatica logs in decay stages 2 and 3. This might suggest, among other things, that decay stages 2 and 3 were most difficult to distinguish by the visual classification system. In some cases, decomposition was found to be more (or less) advanced than ‘suggested’ by the tree surface (i.e., bark or sapwood condition). This could be the result of case hardening (drying out of the outer sapwood) as decomposition took mostly place in open areas (see also Yin 1999). In addition, decomposition of F. sylvatica logs was spatially highly variable. Intact and decomposed patches (with different densities) separated by demarcation lines composed of melanin, which is characteristic of white rot fungi (see also Schwarze et al. 1999; Kahl 2008), were found in direct proximity within the same tree disc. Owing to such patterns of log colonization by fungi, density variation may initially increase with decomposition. For example, an increase in density variation with progressing decomposition, with the maximum in decay stage 3 and a decline towards decay stage 5, was also observed by Yatskov et al. (2003).

The absence of distinct differences in wood density between decay classes has also been observed in other studies. Little change (respectively an overlap) in density between the two least (1 and 2) and most advanced (4 and 5) decay classes has been found also in the study by Yatskov et al. (2003). Similar to our results, no significant difference in density of decay stages 3 and 4 of F. sylvatica were also found by Müller-Using and Bartsch (2009). One main characteristic of log sections in decay stage 4 in our study was the close proximity of highly decomposed material to areas of relatively intact wood. Since the maximum decomposition time for woody debris of F. sylvatica in our study was about 16 years, decay stage 4 comprised mainly log segments with a diameter < 20 cm. Hence, our data may not be representative for larger diameter CWD of beech.

Wood densities for the different tree species converged for advanced decay stages, which is in accordance with other studies (Yatskov et al. 2003). We found no significant differences between species for CWD densities in decay stage 4.

The increase in C concentration with advancing decomposition observed in our study was also found by Müller-Using and Bartsch (2007) for beech. It suggests an increase of lignin and a decrease of cellulose, as lignin has a higher proportion of C than cellulose. The increase in C concentration cannot be explained by a decrease of minerals in relation to C. Similar to our results, C concentrations were lower for angiosperm CWD when compared to gymnosperm CWD in a study that analysed C concentrations of 60 tree species (Harmon et al. 2013). Unlike our results, a decrease in C concentration with increasing decay class was observed for angiosperm CWD in that study. In contrast to C concentration, C content was highest for beech, followed by pine and spruce. This can be explained by the higher density of sound wood.

The pattern of C density (mgC · cm⁻³) and decay stage was similar to that of wood density and decay stage. C density decreased parallel with density and mass loss, as has been found by others (Müller-Using 2005). Carbon density in CWD converged across species with increasing decay (similar to the relationship between density and decay stage). Hence for highly decayed logs, for which it may also be difficult to determine the species origin, one common wood and C density may be assumed.

To assess C in CWD from inventories that record decay stage, our values of dry wood and C density per species and decay stage could be used for calculation purposes. However, the actual assessment of C concentration in inventories appears to be meaningful only for beech, where the difference between the measured C concentration and the default value of 50 % was 3.4 %–4.6 %.